Overview

Dataset statistics

Number of variables116
Number of observations8403
Missing cells41707
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory928.0 B

Variable types

Categorical93
Numeric23

Alerts

ID has a high cardinality: 8403 distinct values High cardinality
year is highly correlated with BP16_1 and 1 other fieldsHigh correlation
sex is highly correlated with HE_ht and 2 other fieldsHigh correlation
HE_ht is highly correlated with sex and 3 other fieldsHigh correlation
HE_wt is highly correlated with HE_ht and 2 other fieldsHigh correlation
HE_BMI is highly correlated with HE_wt and 1 other fieldsHigh correlation
M_2_yr is highly correlated with M_2_rs and 28 other fieldsHigh correlation
M_2_rs is highly correlated with M_2_yr and 28 other fieldsHigh correlation
LQ_1EQL is highly correlated with LQ_2EQL and 17 other fieldsHigh correlation
LQ_2EQL is highly correlated with LQ_1EQL and 32 other fieldsHigh correlation
LQ_3EQL is highly correlated with LQ_1EQL and 23 other fieldsHigh correlation
LQ_4EQL is highly correlated with LQ_1EQL and 17 other fieldsHigh correlation
LQ_5EQL is highly correlated with LQ_1EQL and 29 other fieldsHigh correlation
BD2_1 is highly correlated with BD2_31 and 1 other fieldsHigh correlation
BD2_31 is highly correlated with BD2_1 and 1 other fieldsHigh correlation
dr_month is highly correlated with BD2_1 and 1 other fieldsHigh correlation
BP16_1 is highly correlated with year and 1 other fieldsHigh correlation
BP16_2 is highly correlated with year and 1 other fieldsHigh correlation
BP1 is highly correlated with mh_stressHigh correlation
mh_stress is highly correlated with BP1High correlation
BS1_1 is highly correlated with sex and 2 other fieldsHigh correlation
BS3_1 is highly correlated with sex and 4 other fieldsHigh correlation
BS3_2 is highly correlated with BS3_1 and 2 other fieldsHigh correlation
BS3_3 is highly correlated with BS3_2 and 1 other fieldsHigh correlation
BS12_47 is highly correlated with BS3_3High correlation
sm_presnt is highly correlated with BS3_1 and 1 other fieldsHigh correlation
BE3_71 is highly correlated with M_2_yr and 39 other fieldsHigh correlation
BE3_72 is highly correlated with M_2_yr and 39 other fieldsHigh correlation
BE3_81 is highly correlated with M_2_yr and 35 other fieldsHigh correlation
BE3_82 is highly correlated with M_2_yr and 35 other fieldsHigh correlation
BE3_75 is highly correlated with M_2_yr and 38 other fieldsHigh correlation
BE3_76 is highly correlated with M_2_yr and 38 other fieldsHigh correlation
BE3_85 is highly correlated with LQ_2EQL and 29 other fieldsHigh correlation
BE3_86 is highly correlated with LQ_2EQL and 29 other fieldsHigh correlation
BE3_91 is highly correlated with BE3_71 and 12 other fieldsHigh correlation
BE3_32 is highly correlated with BE3_91High correlation
BE5_1 is highly correlated with BE3_71 and 22 other fieldsHigh correlation
pa_aerobic is highly correlated with BE3_91High correlation
DI1_pr is highly correlated with HE_HPHigh correlation
DI2_pr is highly correlated with HE_HCHOLHigh correlation
DI3_pr is highly correlated with M_2_yr and 30 other fieldsHigh correlation
DI4_pr is highly correlated with DI5_pr and 1 other fieldsHigh correlation
DI5_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DM2_pr is highly correlated with BE3_71 and 13 other fieldsHigh correlation
DM3_pr is highly correlated with M_2_yr and 35 other fieldsHigh correlation
DM4_pr is highly correlated with BE3_71 and 23 other fieldsHigh correlation
DJ2_pr is highly correlated with M_2_yr and 32 other fieldsHigh correlation
DJ4_pr is highly correlated with M_2_yr and 34 other fieldsHigh correlation
DJ6_pr is highly correlated with M_2_yr and 34 other fieldsHigh correlation
DJ8_pr is highly correlated with M_2_yr and 32 other fieldsHigh correlation
DI6_pr is highly correlated with M_2_yr and 33 other fieldsHigh correlation
DF2_pr is highly correlated with M_2_yr and 32 other fieldsHigh correlation
DL1_pr is highly correlated with M_2_yr and 38 other fieldsHigh correlation
DE1_pr is highly correlated with HE_DM_HbA1cHigh correlation
DE2_pr is highly correlated with M_2_yr and 35 other fieldsHigh correlation
DH4_pr is highly correlated with M_2_yr and 35 other fieldsHigh correlation
DC1_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DC2_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
DC3_pr is highly correlated with M_2_yr and 38 other fieldsHigh correlation
DC4_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
DC5_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
DC6_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
DC7_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
DK8_pr is highly correlated with M_2_yr and 38 other fieldsHigh correlation
DK9_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
DK4_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
HE_THfh1 is highly correlated with HE_THfh2 and 17 other fieldsHigh correlation
HE_THfh2 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_THfh3 is highly correlated with HE_THfh1 and 16 other fieldsHigh correlation
HE_HBfh1 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_HBfh2 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_HBfh3 is highly correlated with HE_THfh1 and 16 other fieldsHigh correlation
HE_fh is highly correlated with HE_HPfh2 and 2 other fieldsHigh correlation
HE_HPfh1 is highly correlated with HE_THfh1 and 12 other fieldsHigh correlation
HE_HPfh2 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_HPfh3 is highly correlated with HE_THfh3 and 6 other fieldsHigh correlation
HE_HLfh1 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_HLfh2 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_HLfh3 is highly correlated with HE_THfh1 and 14 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with HE_THfh1 and 14 other fieldsHigh correlation
HE_STRfh1 is highly correlated with HE_THfh1 and 12 other fieldsHigh correlation
HE_STRfh2 is highly correlated with HE_THfh1 and 15 other fieldsHigh correlation
HE_STRfh3 is highly correlated with HE_THfh1 and 14 other fieldsHigh correlation
HE_DMfh1 is highly correlated with HE_THfh1 and 14 other fieldsHigh correlation
HE_DMfh2 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_DMfh3 is highly correlated with HE_THfh3 and 7 other fieldsHigh correlation
HE_HP is highly correlated with DI1_prHigh correlation
HE_obe is highly correlated with HE_wt and 1 other fieldsHigh correlation
HE_DM_HbA1c is highly correlated with DE1_prHigh correlation
HE_HCHOL is highly correlated with DI2_prHigh correlation
L_BR is highly correlated with L_BR_FQHigh correlation
L_LN is highly correlated with L_LN_FQHigh correlation
L_BR_FQ is highly correlated with L_BRHigh correlation
L_LN_FQ is highly correlated with L_LNHigh correlation
sex is highly correlated with HE_ht and 1 other fieldsHigh correlation
HE_ht is highly correlated with sex and 2 other fieldsHigh correlation
HE_wt is highly correlated with HE_ht and 2 other fieldsHigh correlation
HE_BMI is highly correlated with HE_wt and 1 other fieldsHigh correlation
M_2_yr is highly correlated with LQ_1EQL and 15 other fieldsHigh correlation
LQ_1EQL is highly correlated with M_2_yr and 16 other fieldsHigh correlation
LQ_2EQL is highly correlated with M_2_yr and 16 other fieldsHigh correlation
LQ_3EQL is highly correlated with M_2_yr and 16 other fieldsHigh correlation
LQ_4EQL is highly correlated with M_2_yr and 15 other fieldsHigh correlation
LQ_5EQL is highly correlated with M_2_yr and 16 other fieldsHigh correlation
BO1_1 is highly correlated with BP1 and 3 other fieldsHigh correlation
BD2_1 is highly correlated with BD2_31 and 1 other fieldsHigh correlation
BD2_31 is highly correlated with BD2_1 and 1 other fieldsHigh correlation
dr_month is highly correlated with BD2_1 and 1 other fieldsHigh correlation
BP16_1 is highly correlated with BP16_2High correlation
BP16_2 is highly correlated with BP16_1High correlation
BP1 is highly correlated with BO1_1 and 4 other fieldsHigh correlation
mh_stress is highly correlated with BP1High correlation
BS1_1 is highly correlated with BO1_1 and 4 other fieldsHigh correlation
BS3_1 is highly correlated with sex and 4 other fieldsHigh correlation
BS3_2 is highly correlated with BS3_1 and 1 other fieldsHigh correlation
BS9_2 is highly correlated with BO1_1 and 3 other fieldsHigh correlation
BS13 is highly correlated with BO1_1 and 3 other fieldsHigh correlation
sm_presnt is highly correlated with BS3_1 and 1 other fieldsHigh correlation
BE3_71 is highly correlated with M_2_yr and 15 other fieldsHigh correlation
BE3_72 is highly correlated with M_2_yr and 13 other fieldsHigh correlation
BE3_81 is highly correlated with M_2_yr and 15 other fieldsHigh correlation
BE3_75 is highly correlated with M_2_yr and 16 other fieldsHigh correlation
BE3_76 is highly correlated with BE3_75 and 1 other fieldsHigh correlation
BE3_85 is highly correlated with M_2_yr and 17 other fieldsHigh correlation
BE3_91 is highly correlated with M_2_yr and 17 other fieldsHigh correlation
BE8_1 is highly correlated with M_2_yr and 13 other fieldsHigh correlation
BE3_31 is highly correlated with M_2_yr and 15 other fieldsHigh correlation
BE3_32 is highly correlated with LQ_1EQL and 6 other fieldsHigh correlation
BE5_1 is highly correlated with M_2_yr and 13 other fieldsHigh correlation
pa_aerobic is highly correlated with BE3_91High correlation
DI1_pr is highly correlated with HE_HPHigh correlation
DI2_pr is highly correlated with HE_HCHOLHigh correlation
DI4_pr is highly correlated with DI5_pr and 1 other fieldsHigh correlation
DI5_pr is highly correlated with DI4_prHigh correlation
DI6_pr is highly correlated with DI4_prHigh correlation
DE1_pr is highly correlated with HE_DM_HbA1cHigh correlation
DC2_pr is highly correlated with M_2_yr and 11 other fieldsHigh correlation
DK9_pr is highly correlated with M_2_yr and 10 other fieldsHigh correlation
HE_THfh1 is highly correlated with HE_THfh2 and 20 other fieldsHigh correlation
HE_THfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_THfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HBfh1 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HBfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HBfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_fh is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HPfh1 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HPfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HPfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HLfh1 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HLfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HLfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_STRfh1 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_STRfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_STRfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_DMfh1 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_DMfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_DMfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HP is highly correlated with DI1_prHigh correlation
HE_obe is highly correlated with HE_wt and 1 other fieldsHigh correlation
HE_DM_HbA1c is highly correlated with DE1_prHigh correlation
HE_HCHOL is highly correlated with DI2_prHigh correlation
T_NQ_OCP is highly correlated with T_Q_VNHigh correlation
T_Q_VN is highly correlated with T_NQ_OCPHigh correlation
L_BR is highly correlated with L_BR_FQHigh correlation
L_LN is highly correlated with L_LN_FQHigh correlation
L_BR_FQ is highly correlated with L_BRHigh correlation
L_LN_FQ is highly correlated with L_LNHigh correlation
year is highly correlated with BP16_1 and 1 other fieldsHigh correlation
sex is highly correlated with HE_ht and 2 other fieldsHigh correlation
HE_ht is highly correlated with sexHigh correlation
HE_wt is highly correlated with HE_BMI and 1 other fieldsHigh correlation
HE_BMI is highly correlated with HE_wt and 1 other fieldsHigh correlation
M_2_yr is highly correlated with M_2_rs and 28 other fieldsHigh correlation
M_2_rs is highly correlated with M_2_yr and 28 other fieldsHigh correlation
LQ_1EQL is highly correlated with LQ_2EQL and 11 other fieldsHigh correlation
LQ_2EQL is highly correlated with LQ_1EQL and 31 other fieldsHigh correlation
LQ_3EQL is highly correlated with LQ_1EQL and 20 other fieldsHigh correlation
LQ_4EQL is highly correlated with LQ_1EQL and 6 other fieldsHigh correlation
LQ_5EQL is highly correlated with LQ_1EQL and 24 other fieldsHigh correlation
BD2_1 is highly correlated with BD2_31 and 1 other fieldsHigh correlation
BD2_31 is highly correlated with BD2_1 and 1 other fieldsHigh correlation
dr_month is highly correlated with BD2_1 and 1 other fieldsHigh correlation
BP16_1 is highly correlated with year and 1 other fieldsHigh correlation
BP16_2 is highly correlated with year and 1 other fieldsHigh correlation
BP1 is highly correlated with mh_stressHigh correlation
mh_stress is highly correlated with BP1High correlation
BS1_1 is highly correlated with sex and 1 other fieldsHigh correlation
BS3_1 is highly correlated with sex and 3 other fieldsHigh correlation
BS3_2 is highly correlated with BS3_1 and 2 other fieldsHigh correlation
BS3_3 is highly correlated with BS3_2 and 1 other fieldsHigh correlation
BS12_47 is highly correlated with BS3_3High correlation
sm_presnt is highly correlated with BS3_1 and 1 other fieldsHigh correlation
BE3_71 is highly correlated with M_2_yr and 39 other fieldsHigh correlation
BE3_72 is highly correlated with M_2_yr and 39 other fieldsHigh correlation
BE3_81 is highly correlated with M_2_yr and 34 other fieldsHigh correlation
BE3_82 is highly correlated with M_2_yr and 34 other fieldsHigh correlation
BE3_75 is highly correlated with M_2_yr and 35 other fieldsHigh correlation
BE3_76 is highly correlated with M_2_yr and 35 other fieldsHigh correlation
BE3_85 is highly correlated with LQ_2EQL and 27 other fieldsHigh correlation
BE3_86 is highly correlated with LQ_2EQL and 27 other fieldsHigh correlation
BE3_91 is highly correlated with BE3_71 and 4 other fieldsHigh correlation
BE5_1 is highly correlated with BE3_71 and 16 other fieldsHigh correlation
pa_aerobic is highly correlated with BE3_91High correlation
DI1_pr is highly correlated with HE_HPHigh correlation
DI2_pr is highly correlated with HE_HCHOLHigh correlation
DI3_pr is highly correlated with M_2_yr and 28 other fieldsHigh correlation
DI4_pr is highly correlated with DI5_pr and 1 other fieldsHigh correlation
DI5_pr is highly correlated with M_2_yr and 33 other fieldsHigh correlation
DM2_pr is highly correlated with BE3_71 and 10 other fieldsHigh correlation
DM3_pr is highly correlated with M_2_yr and 33 other fieldsHigh correlation
DM4_pr is highly correlated with BE3_71 and 13 other fieldsHigh correlation
DJ2_pr is highly correlated with M_2_yr and 31 other fieldsHigh correlation
DJ4_pr is highly correlated with M_2_yr and 31 other fieldsHigh correlation
DJ6_pr is highly correlated with M_2_yr and 31 other fieldsHigh correlation
DJ8_pr is highly correlated with M_2_yr and 31 other fieldsHigh correlation
DI6_pr is highly correlated with M_2_yr and 32 other fieldsHigh correlation
DF2_pr is highly correlated with M_2_yr and 28 other fieldsHigh correlation
DL1_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DE1_pr is highly correlated with HE_DM_HbA1cHigh correlation
DE2_pr is highly correlated with M_2_yr and 32 other fieldsHigh correlation
DH4_pr is highly correlated with M_2_yr and 32 other fieldsHigh correlation
DC1_pr is highly correlated with M_2_yr and 35 other fieldsHigh correlation
DC2_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
DC3_pr is highly correlated with M_2_yr and 35 other fieldsHigh correlation
DC4_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DC5_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DC6_pr is highly correlated with M_2_yr and 37 other fieldsHigh correlation
DC7_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DK8_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DK9_pr is highly correlated with M_2_yr and 38 other fieldsHigh correlation
DK4_pr is highly correlated with M_2_yr and 37 other fieldsHigh correlation
HE_THfh1 is highly correlated with HE_THfh2 and 17 other fieldsHigh correlation
HE_THfh2 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_THfh3 is highly correlated with HE_THfh1 and 14 other fieldsHigh correlation
HE_HBfh1 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_HBfh2 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_HBfh3 is highly correlated with HE_THfh1 and 15 other fieldsHigh correlation
HE_fh is highly correlated with HE_HPfh2 and 1 other fieldsHigh correlation
HE_HPfh1 is highly correlated with HE_THfh1 and 12 other fieldsHigh correlation
HE_HPfh2 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_HPfh3 is highly correlated with HE_THfh3 and 6 other fieldsHigh correlation
HE_HLfh1 is highly correlated with HE_THfh1 and 16 other fieldsHigh correlation
HE_HLfh2 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_HLfh3 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with HE_THfh1 and 16 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_STRfh1 is highly correlated with HE_THfh1 and 12 other fieldsHigh correlation
HE_STRfh2 is highly correlated with HE_THfh1 and 12 other fieldsHigh correlation
HE_STRfh3 is highly correlated with HE_THfh1 and 11 other fieldsHigh correlation
HE_DMfh1 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_DMfh2 is highly correlated with HE_THfh1 and 14 other fieldsHigh correlation
HE_DMfh3 is highly correlated with HE_THfh3 and 5 other fieldsHigh correlation
HE_HP is highly correlated with DI1_prHigh correlation
HE_obe is highly correlated with HE_wt and 1 other fieldsHigh correlation
HE_DM_HbA1c is highly correlated with DE1_prHigh correlation
HE_HCHOL is highly correlated with DI2_prHigh correlation
L_BR is highly correlated with L_BR_FQHigh correlation
L_LN is highly correlated with L_LN_FQHigh correlation
L_BR_FQ is highly correlated with L_BRHigh correlation
L_LN_FQ is highly correlated with L_LNHigh correlation
BS8_2 is highly correlated with BS1_1 and 6 other fieldsHigh correlation
L_BR_FQ is highly correlated with L_BRHigh correlation
LQ_4EQL is highly correlated with DC2_pr and 33 other fieldsHigh correlation
DC2_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DI3_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
LQ_1EQL is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DJ8_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
L_LN is highly correlated with L_LN_FQHigh correlation
mh_stress is highly correlated with BP1High correlation
HE_DMfh3 is highly correlated with HE_HLfh3 and 6 other fieldsHigh correlation
DM4_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DJ6_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
HE_HLfh3 is highly correlated with HE_DMfh3 and 6 other fieldsHigh correlation
HE_HLfh2 is highly correlated with HE_THfh1 and 12 other fieldsHigh correlation
HE_fh is highly correlated with HE_DMfh3 and 9 other fieldsHigh correlation
DM2_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
M_2_yr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
BS1_1 is highly correlated with BS8_2 and 7 other fieldsHigh correlation
DM3_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DF2_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DJ2_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DI1_pr is highly correlated with HE_HPHigh correlation
sm_presnt is highly correlated with BS3_1High correlation
DE2_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
BO2_1 is highly correlated with BS8_2 and 4 other fieldsHigh correlation
BE3_81 is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DI5_pr is highly correlated with LQ_4EQL and 34 other fieldsHigh correlation
HE_THfh1 is highly correlated with HE_HLfh2 and 12 other fieldsHigh correlation
DC3_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
HE_HBfh1 is highly correlated with HE_HLfh2 and 12 other fieldsHigh correlation
DC6_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
HE_DMfh1 is highly correlated with HE_HLfh2 and 12 other fieldsHigh correlation
BO1_1 is highly correlated with BO2_1 and 1 other fieldsHigh correlation
DK4_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
LQ_3EQL is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DC7_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DL1_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
HE_HP is highly correlated with DI1_prHigh correlation
DI6_pr is highly correlated with LQ_4EQL and 34 other fieldsHigh correlation
BS13 is highly correlated with BS8_2 and 7 other fieldsHigh correlation
DC1_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
BE3_85 is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with HE_HLfh2 and 12 other fieldsHigh correlation
HE_HPfh2 is highly correlated with HE_HLfh2 and 13 other fieldsHigh correlation
LQ_5EQL is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DI2_pr is highly correlated with HE_HCHOLHigh correlation
DC5_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
HE_HPfh3 is highly correlated with HE_DMfh3 and 6 other fieldsHigh correlation
DH4_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DJ4_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
LQ_2EQL is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
BS9_2 is highly correlated with BS8_2 and 6 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with HE_DMfh3 and 6 other fieldsHigh correlation
HE_DM_HbA1c is highly correlated with DE1_prHigh correlation
DE1_pr is highly correlated with HE_DM_HbA1cHigh correlation
T_Q_VN is highly correlated with T_NQ_OCPHigh correlation
BP1 is highly correlated with BS8_2 and 4 other fieldsHigh correlation
HE_HBfh2 is highly correlated with HE_HLfh2 and 13 other fieldsHigh correlation
HE_THfh3 is highly correlated with HE_DMfh3 and 6 other fieldsHigh correlation
BE3_91 is highly correlated with LQ_4EQL and 34 other fieldsHigh correlation
HE_HBfh3 is highly correlated with HE_DMfh3 and 7 other fieldsHigh correlation
L_LN_FQ is highly correlated with L_LNHigh correlation
BS12_47 is highly correlated with BS8_2 and 4 other fieldsHigh correlation
DK8_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DC4_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
L_BR is highly correlated with L_BR_FQHigh correlation
HE_THfh2 is highly correlated with HE_HLfh2 and 12 other fieldsHigh correlation
HE_DMfh2 is highly correlated with HE_HLfh2 and 13 other fieldsHigh correlation
DK9_pr is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
HE_STRfh3 is highly correlated with HE_DMfh3 and 6 other fieldsHigh correlation
BE3_71 is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with HE_HLfh2 and 12 other fieldsHigh correlation
sex is highly correlated with BS1_1 and 1 other fieldsHigh correlation
HE_STRfh1 is highly correlated with HE_HLfh2 and 12 other fieldsHigh correlation
T_NQ_OCP is highly correlated with T_Q_VNHigh correlation
pa_aerobic is highly correlated with BE3_91High correlation
HE_HLfh1 is highly correlated with HE_HLfh2 and 12 other fieldsHigh correlation
BE3_75 is highly correlated with LQ_4EQL and 33 other fieldsHigh correlation
DI4_pr is highly correlated with DI5_pr and 1 other fieldsHigh correlation
HE_STRfh2 is highly correlated with HE_HLfh2 and 13 other fieldsHigh correlation
HE_HPfh1 is highly correlated with HE_HLfh2 and 12 other fieldsHigh correlation
HE_HCHOL is highly correlated with DI2_prHigh correlation
BS3_1 is highly correlated with BS8_2 and 6 other fieldsHigh correlation
sex is highly correlated with HE_ht and 6 other fieldsHigh correlation
HE_ht is highly correlated with sex and 3 other fieldsHigh correlation
HE_wt is highly correlated with sex and 3 other fieldsHigh correlation
HE_BMI is highly correlated with HE_wt and 2 other fieldsHigh correlation
M_2_yr is highly correlated with M_2_rs and 46 other fieldsHigh correlation
M_2_rs is highly correlated with M_2_yr and 45 other fieldsHigh correlation
LQ_1EQL is highly correlated with M_2_yr and 46 other fieldsHigh correlation
LQ_2EQL is highly correlated with M_2_yr and 46 other fieldsHigh correlation
LQ_3EQL is highly correlated with M_2_yr and 46 other fieldsHigh correlation
LQ_4EQL is highly correlated with M_2_yr and 46 other fieldsHigh correlation
LQ_5EQL is highly correlated with M_2_yr and 46 other fieldsHigh correlation
BO1_1 is highly correlated with BO2_1 and 13 other fieldsHigh correlation
BO2_1 is highly correlated with HE_BMI and 13 other fieldsHigh correlation
BD1_11 is highly correlated with sex and 15 other fieldsHigh correlation
BD2_1 is highly correlated with BO1_1 and 13 other fieldsHigh correlation
BD2_31 is highly correlated with BO1_1 and 13 other fieldsHigh correlation
dr_month is highly correlated with sex and 4 other fieldsHigh correlation
BP16_1 is highly correlated with M_2_yr and 32 other fieldsHigh correlation
BP16_2 is highly correlated with M_2_rs and 13 other fieldsHigh correlation
BP1 is highly correlated with BO1_1 and 13 other fieldsHigh correlation
mh_stress is highly correlated with BP1High correlation
BS1_1 is highly correlated with sex and 46 other fieldsHigh correlation
BS3_1 is highly correlated with sex and 15 other fieldsHigh correlation
BS3_2 is highly correlated with M_2_rs and 25 other fieldsHigh correlation
BS3_3 is highly correlated with BO1_1 and 12 other fieldsHigh correlation
BS12_47 is highly correlated with BO1_1 and 12 other fieldsHigh correlation
BS8_2 is highly correlated with M_2_yr and 42 other fieldsHigh correlation
BS9_2 is highly correlated with M_2_yr and 32 other fieldsHigh correlation
BS13 is highly correlated with M_2_rs and 24 other fieldsHigh correlation
sm_presnt is highly correlated with BS3_1 and 1 other fieldsHigh correlation
BE3_71 is highly correlated with M_2_yr and 45 other fieldsHigh correlation
BE3_72 is highly correlated with M_2_yr and 45 other fieldsHigh correlation
BE3_81 is highly correlated with M_2_yr and 45 other fieldsHigh correlation
BE3_82 is highly correlated with M_2_yr and 45 other fieldsHigh correlation
BE3_75 is highly correlated with M_2_yr and 45 other fieldsHigh correlation
BE3_76 is highly correlated with M_2_yr and 45 other fieldsHigh correlation
BE3_85 is highly correlated with M_2_yr and 46 other fieldsHigh correlation
BE3_86 is highly correlated with M_2_yr and 46 other fieldsHigh correlation
BE3_91 is highly correlated with M_2_yr and 46 other fieldsHigh correlation
BE8_1 is highly correlated with M_2_yr and 44 other fieldsHigh correlation
BE3_31 is highly correlated with M_2_yr and 47 other fieldsHigh correlation
BE3_32 is highly correlated with M_2_yr and 45 other fieldsHigh correlation
BE5_1 is highly correlated with M_2_yr and 42 other fieldsHigh correlation
pa_aerobic is highly correlated with BE3_85 and 2 other fieldsHigh correlation
DI1_pr is highly correlated with DI2_pr and 2 other fieldsHigh correlation
DI2_pr is highly correlated with DI1_pr and 2 other fieldsHigh correlation
DI3_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DI4_pr is highly correlated with DI5_pr and 1 other fieldsHigh correlation
DI5_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DM2_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DM3_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DM4_pr is highly correlated with sex and 48 other fieldsHigh correlation
DJ2_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
DJ4_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
DJ6_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
DJ8_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
DI6_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DF2_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
DL1_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
DE1_pr is highly correlated with DI1_pr and 2 other fieldsHigh correlation
DE2_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DH4_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
DC1_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DC2_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
DC3_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DC4_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
DC5_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DC6_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DC7_pr is highly correlated with M_2_yr and 45 other fieldsHigh correlation
DK8_pr is highly correlated with M_2_yr and 47 other fieldsHigh correlation
DK9_pr is highly correlated with M_2_yr and 47 other fieldsHigh correlation
DK4_pr is highly correlated with M_2_yr and 46 other fieldsHigh correlation
HE_THfh1 is highly correlated with HE_THfh2 and 13 other fieldsHigh correlation
HE_THfh2 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_THfh3 is highly correlated with HE_HBfh3 and 6 other fieldsHigh correlation
HE_HBfh1 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_HBfh2 is highly correlated with HE_THfh1 and 14 other fieldsHigh correlation
HE_HBfh3 is highly correlated with HE_THfh3 and 7 other fieldsHigh correlation
HE_fh is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HPfh1 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_HPfh2 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_HPfh3 is highly correlated with HE_THfh3 and 6 other fieldsHigh correlation
HE_HLfh1 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_HLfh2 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_HLfh3 is highly correlated with HE_THfh3 and 6 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with HE_THfh3 and 6 other fieldsHigh correlation
HE_STRfh1 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_STRfh2 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_STRfh3 is highly correlated with HE_THfh3 and 6 other fieldsHigh correlation
HE_DMfh1 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_DMfh2 is highly correlated with HE_THfh1 and 13 other fieldsHigh correlation
HE_DMfh3 is highly correlated with HE_THfh3 and 6 other fieldsHigh correlation
HE_HP is highly correlated with DI1_prHigh correlation
HE_obe is highly correlated with HE_wt and 1 other fieldsHigh correlation
HE_DM_HbA1c is highly correlated with DE1_prHigh correlation
HE_HCHOL is highly correlated with DI2_prHigh correlation
HE_hepaB is highly correlated with DK8_prHigh correlation
HE_hepaC is highly correlated with DK9_prHigh correlation
T_NQ_OCP is highly correlated with T_Q_VNHigh correlation
T_Q_VN is highly correlated with T_NQ_OCPHigh correlation
L_BR is highly correlated with L_BR_FQHigh correlation
L_LN is highly correlated with L_LN_FQHigh correlation
L_DN is highly correlated with L_DN_FQHigh correlation
L_BR_FQ is highly correlated with L_BRHigh correlation
L_LN_FQ is highly correlated with L_LNHigh correlation
L_DN_FQ is highly correlated with L_DNHigh correlation
HE_ht has 354 (4.2%) missing values Missing
HE_wt has 245 (2.9%) missing values Missing
HE_BMI has 355 (4.2%) missing values Missing
M_2_yr has 231 (2.7%) missing values Missing
M_2_rs has 231 (2.7%) missing values Missing
LQ_1EQL has 231 (2.7%) missing values Missing
LQ_2EQL has 231 (2.7%) missing values Missing
LQ_3EQL has 231 (2.7%) missing values Missing
LQ_4EQL has 231 (2.7%) missing values Missing
LQ_5EQL has 231 (2.7%) missing values Missing
BO1_1 has 231 (2.7%) missing values Missing
BO2_1 has 231 (2.7%) missing values Missing
BD1_11 has 231 (2.7%) missing values Missing
BD2_1 has 231 (2.7%) missing values Missing
BD2_31 has 231 (2.7%) missing values Missing
dr_month has 392 (4.7%) missing values Missing
BP16_1 has 325 (3.9%) missing values Missing
BP16_2 has 324 (3.9%) missing values Missing
BP1 has 231 (2.7%) missing values Missing
mh_stress has 420 (5.0%) missing values Missing
BS1_1 has 231 (2.7%) missing values Missing
BS3_1 has 231 (2.7%) missing values Missing
BS3_2 has 231 (2.7%) missing values Missing
BS3_3 has 231 (2.7%) missing values Missing
BS12_47 has 231 (2.7%) missing values Missing
BS8_2 has 231 (2.7%) missing values Missing
BS9_2 has 231 (2.7%) missing values Missing
BS13 has 231 (2.7%) missing values Missing
sm_presnt has 408 (4.9%) missing values Missing
BE3_71 has 231 (2.7%) missing values Missing
BE3_72 has 231 (2.7%) missing values Missing
BE3_81 has 231 (2.7%) missing values Missing
BE3_82 has 231 (2.7%) missing values Missing
BE3_75 has 231 (2.7%) missing values Missing
BE3_76 has 231 (2.7%) missing values Missing
BE3_85 has 231 (2.7%) missing values Missing
BE3_86 has 231 (2.7%) missing values Missing
BE3_91 has 231 (2.7%) missing values Missing
BE8_1 has 231 (2.7%) missing values Missing
BE3_31 has 231 (2.7%) missing values Missing
BE3_32 has 231 (2.7%) missing values Missing
BE5_1 has 231 (2.7%) missing values Missing
pa_aerobic has 1012 (12.0%) missing values Missing
DI1_pr has 231 (2.7%) missing values Missing
DI2_pr has 231 (2.7%) missing values Missing
DI3_pr has 231 (2.7%) missing values Missing
DI4_pr has 906 (10.8%) missing values Missing
DI5_pr has 231 (2.7%) missing values Missing
DM2_pr has 231 (2.7%) missing values Missing
DM3_pr has 231 (2.7%) missing values Missing
DM4_pr has 231 (2.7%) missing values Missing
DJ2_pr has 231 (2.7%) missing values Missing
DJ4_pr has 231 (2.7%) missing values Missing
DJ6_pr has 231 (2.7%) missing values Missing
DJ8_pr has 231 (2.7%) missing values Missing
DI6_pr has 231 (2.7%) missing values Missing
DF2_pr has 231 (2.7%) missing values Missing
DL1_pr has 231 (2.7%) missing values Missing
DE1_pr has 231 (2.7%) missing values Missing
DE2_pr has 231 (2.7%) missing values Missing
DH4_pr has 231 (2.7%) missing values Missing
DC1_pr has 231 (2.7%) missing values Missing
DC2_pr has 231 (2.7%) missing values Missing
DC3_pr has 231 (2.7%) missing values Missing
DC4_pr has 231 (2.7%) missing values Missing
DC5_pr has 231 (2.7%) missing values Missing
DC6_pr has 231 (2.7%) missing values Missing
DC7_pr has 231 (2.7%) missing values Missing
DK8_pr has 231 (2.7%) missing values Missing
DK9_pr has 231 (2.7%) missing values Missing
DK4_pr has 231 (2.7%) missing values Missing
HE_Upro has 612 (7.3%) missing values Missing
HE_THfh1 has 261 (3.1%) missing values Missing
HE_THfh2 has 261 (3.1%) missing values Missing
HE_THfh3 has 261 (3.1%) missing values Missing
HE_HBfh1 has 261 (3.1%) missing values Missing
HE_HBfh2 has 261 (3.1%) missing values Missing
HE_HBfh3 has 261 (3.1%) missing values Missing
HE_fh has 261 (3.1%) missing values Missing
HE_HPfh1 has 261 (3.1%) missing values Missing
HE_HPfh2 has 261 (3.1%) missing values Missing
HE_HPfh3 has 261 (3.1%) missing values Missing
HE_HLfh1 has 261 (3.1%) missing values Missing
HE_HLfh2 has 261 (3.1%) missing values Missing
HE_HLfh3 has 261 (3.1%) missing values Missing
HE_IHDfh1 has 261 (3.1%) missing values Missing
HE_IHDfh2 has 261 (3.1%) missing values Missing
HE_IHDfh3 has 261 (3.1%) missing values Missing
HE_STRfh1 has 261 (3.1%) missing values Missing
HE_STRfh2 has 261 (3.1%) missing values Missing
HE_STRfh3 has 261 (3.1%) missing values Missing
HE_DMfh1 has 261 (3.1%) missing values Missing
HE_DMfh2 has 261 (3.1%) missing values Missing
HE_DMfh3 has 261 (3.1%) missing values Missing
HE_HP has 280 (3.3%) missing values Missing
HE_obe has 355 (4.2%) missing values Missing
HE_DM_HbA1c has 1034 (12.3%) missing values Missing
HE_HCHOL has 1032 (12.3%) missing values Missing
HE_HTG has 1765 (21.0%) missing values Missing
HE_hepaB has 698 (8.3%) missing values Missing
HE_hepaC has 698 (8.3%) missing values Missing
HE_anem has 719 (8.6%) missing values Missing
T_NQ_OCP has 1125 (13.4%) missing values Missing
T_Q_VN has 1125 (13.4%) missing values Missing
L_BR has 965 (11.5%) missing values Missing
L_LN has 965 (11.5%) missing values Missing
L_DN has 965 (11.5%) missing values Missing
L_BR_FQ has 959 (11.4%) missing values Missing
L_LN_FQ has 959 (11.4%) missing values Missing
L_DN_FQ has 959 (11.4%) missing values Missing
L_OUT_FQ has 959 (11.4%) missing values Missing
LS_1YR has 959 (11.4%) missing values Missing
ID is uniformly distributed Uniform
ID has unique values Unique
BE3_32 has 2858 (34.0%) zeros Zeros
HE_Upro has 6561 (78.1%) zeros Zeros

Reproduction

Analysis started2022-04-28 03:57:13.070301
Analysis finished2022-04-28 04:00:22.242765
Duration3 minutes and 9.17 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct8403
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
b'E756235701'
 
1
b'A612237401'
 
1
b'C751247602'
 
1
b'N653268001'
 
1
b'C752361501'
 
1
Other values (8398)
8398 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8403 ?
Unique (%)100.0%

Sample

1st rowb'A651228902'
2nd rowb'A651417601'
3rd rowb'A652228901'
4th rowb'A653184701'
5th rowb'A653235705'

Common Values

ValueCountFrequency (%)
b'E756235701'1
 
< 0.1%
b'A612237401'1
 
< 0.1%
b'C751247602'1
 
< 0.1%
b'N653268001'1
 
< 0.1%
b'C752361501'1
 
< 0.1%
b'H809327501'1
 
< 0.1%
b'N805361503'1
 
< 0.1%
b'H804322401'1
 
< 0.1%
b'N601186402'1
 
< 0.1%
b'E652339402'1
 
< 0.1%
Other values (8393)8393
99.9%

Length

2022-04-28T13:00:22.341502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b'c6073428011
 
< 0.1%
b'a7140238021
 
< 0.1%
b'b6522476021
 
< 0.1%
b'm6073683021
 
< 0.1%
b'a7300291021
 
< 0.1%
b'o6591949011
 
< 0.1%
b'n6583666021
 
< 0.1%
b'a6044159021
 
< 0.1%
b'n7040271021
 
< 0.1%
b'b7582731011
 
< 0.1%
Other values (8393)8393
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2019.0
1735 
2020.0
1712 
2017.0
1671 
2018.0
1653 
2016.0
1632 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016.0
2nd row2016.0
3rd row2016.0
4th row2016.0
5th row2016.0

Common Values

ValueCountFrequency (%)
2019.01735
20.6%
2020.01712
20.4%
2017.01671
19.9%
2018.01653
19.7%
2016.01632
19.4%

Length

2022-04-28T13:00:22.477358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:22.535257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2019.01735
20.6%
2020.01712
20.4%
2017.01671
19.9%
2018.01653
19.7%
2016.01632
19.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sex
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2.0
4785 
1.0
3618 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.04785
56.9%
1.03618
43.1%

Length

2022-04-28T13:00:23.138468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:23.191931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.04785
56.9%
1.03618
43.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.0092824
Minimum65
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:23.238794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile65
Q169
median73
Q378
95-th percentile80
Maximum80
Range15
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.108991518
Coefficient of variation (CV)0.06997728714
Kurtosis-1.35643342
Mean73.0092824
Median Absolute Deviation (MAD)5
Skewness-0.004627055643
Sum613497
Variance26.10179433
MonotonicityNot monotonic
2022-04-28T13:00:23.339228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
801604
19.1%
65588
 
7.0%
69528
 
6.3%
66525
 
6.2%
70503
 
6.0%
67491
 
5.8%
68491
 
5.8%
71473
 
5.6%
72469
 
5.6%
73451
 
5.4%
Other values (6)2280
27.1%
ValueCountFrequency (%)
65588
7.0%
66525
6.2%
67491
5.8%
68491
5.8%
69528
6.3%
70503
6.0%
71473
5.6%
72469
5.6%
73451
5.4%
74406
4.8%
ValueCountFrequency (%)
801604
19.1%
79302
 
3.6%
78352
 
4.2%
77435
 
5.2%
76378
 
4.5%
75407
 
4.8%
74406
 
4.8%
73451
 
5.4%
72469
 
5.6%
71473
 
5.6%

HE_ht
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct472
Distinct (%)5.9%
Missing354
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean157.9394459
Minimum128.4
Maximum184.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:23.456684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum128.4
5-th percentile144.3
Q1151.2
median157.3
Q3164.8
95-th percentile172.5
Maximum184.1
Range55.7
Interquartile range (IQR)13.6

Descriptive statistics

Standard deviation8.881939269
Coefficient of variation (CV)0.0562363583
Kurtosis-0.564560387
Mean157.9394459
Median Absolute Deviation (MAD)6.7
Skewness0.09674715075
Sum1271254.6
Variance78.88884517
MonotonicityNot monotonic
2022-04-28T13:00:23.576632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15650
 
0.6%
154.249
 
0.6%
150.245
 
0.5%
15542
 
0.5%
155.642
 
0.5%
154.641
 
0.5%
153.241
 
0.5%
155.841
 
0.5%
155.540
 
0.5%
153.340
 
0.5%
Other values (462)7618
90.7%
(Missing)354
 
4.2%
ValueCountFrequency (%)
128.41
< 0.1%
128.91
< 0.1%
130.11
< 0.1%
130.51
< 0.1%
130.71
< 0.1%
131.31
< 0.1%
131.41
< 0.1%
132.11
< 0.1%
132.41
< 0.1%
132.51
< 0.1%
ValueCountFrequency (%)
184.11
< 0.1%
183.91
< 0.1%
183.61
< 0.1%
183.21
< 0.1%
182.81
< 0.1%
182.71
< 0.1%
1821
< 0.1%
181.61
< 0.1%
181.51
< 0.1%
181.21
< 0.1%

HE_wt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct566
Distinct (%)6.9%
Missing245
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean60.21453788
Minimum31.3
Maximum105.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:23.694655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum31.3
5-th percentile44.6
Q153.2
median59.8
Q366.8
95-th percentile77.4
Maximum105.4
Range74.1
Interquartile range (IQR)13.6

Descriptive statistics

Standard deviation10.10582619
Coefficient of variation (CV)0.167830337
Kurtosis0.1760581768
Mean60.21453788
Median Absolute Deviation (MAD)6.8
Skewness0.3074201246
Sum491230.2
Variance102.1277229
MonotonicityNot monotonic
2022-04-28T13:00:24.222560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6048
 
0.6%
54.342
 
0.5%
56.142
 
0.5%
62.142
 
0.5%
59.842
 
0.5%
61.641
 
0.5%
54.141
 
0.5%
57.541
 
0.5%
54.540
 
0.5%
55.540
 
0.5%
Other values (556)7739
92.1%
(Missing)245
 
2.9%
ValueCountFrequency (%)
31.31
< 0.1%
31.81
< 0.1%
32.21
< 0.1%
32.71
< 0.1%
32.91
< 0.1%
33.22
< 0.1%
33.52
< 0.1%
33.71
< 0.1%
34.12
< 0.1%
34.32
< 0.1%
ValueCountFrequency (%)
105.41
< 0.1%
104.41
< 0.1%
102.81
< 0.1%
102.11
< 0.1%
99.31
< 0.1%
98.41
< 0.1%
981
< 0.1%
97.91
< 0.1%
96.21
< 0.1%
961
< 0.1%

HE_BMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7659
Distinct (%)95.2%
Missing355
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean24.1143128
Minimum11.44342217
Maximum41.49384293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:24.418481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11.44342217
5-th percentile19.13618695
Q122.01094872
median24.00092729
Q326.03068509
95-th percentile29.5692718
Maximum41.49384293
Range30.05042075
Interquartile range (IQR)4.019736373

Descriptive statistics

Standard deviation3.194702821
Coefficient of variation (CV)0.1324816033
Kurtosis0.7950821933
Mean24.1143128
Median Absolute Deviation (MAD)2.011964867
Skewness0.3805309392
Sum194071.9894
Variance10.20612612
MonotonicityNot monotonic
2022-04-28T13:00:24.596180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.542490644
 
< 0.1%
21.962020643
 
< 0.1%
26.722992843
 
< 0.1%
24.592404363
 
< 0.1%
28.035010653
 
< 0.1%
20.565539733
 
< 0.1%
24.370605643
 
< 0.1%
27.374110453
 
< 0.1%
21.149851813
 
< 0.1%
22.614036083
 
< 0.1%
Other values (7649)8017
95.4%
(Missing)355
 
4.2%
ValueCountFrequency (%)
11.443422171
< 0.1%
13.977505581
< 0.1%
14.164058191
< 0.1%
14.710716631
< 0.1%
14.7643561
< 0.1%
14.796376491
< 0.1%
14.844239721
< 0.1%
15.031507031
< 0.1%
15.175783191
< 0.1%
15.30332551
< 0.1%
ValueCountFrequency (%)
41.493842931
< 0.1%
39.78453491
< 0.1%
38.9750111
< 0.1%
38.944150891
< 0.1%
38.063659741
< 0.1%
37.304866851
< 0.1%
37.06443131
< 0.1%
36.647794361
< 0.1%
36.591020271
< 0.1%
36.512564211
< 0.1%

M_2_yr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
2.0
6619 
1.0
732 
9.0
698 
3.0
 
123

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.06619
78.8%
1.0732
 
8.7%
9.0698
 
8.3%
3.0123
 
1.5%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:24.742944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:24.822871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.06619
81.0%
1.0732
 
9.0%
9.0698
 
8.5%
3.0123
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

M_2_rs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean81.34569261
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:24.901652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q188
median88
Q388
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.72625904
Coefficient of variation (CV)0.3039651916
Kurtosis6.040472252
Mean81.34569261
Median Absolute Deviation (MAD)0
Skewness-2.794667832
Sum664757
Variance611.3878862
MonotonicityNot monotonic
2022-04-28T13:00:25.006615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
886742
80.2%
99699
 
8.3%
3249
 
3.0%
2174
 
2.1%
1139
 
1.7%
464
 
0.8%
853
 
0.6%
742
 
0.5%
58
 
0.1%
62
 
< 0.1%
(Missing)231
 
2.7%
ValueCountFrequency (%)
1139
 
1.7%
2174
 
2.1%
3249
 
3.0%
464
 
0.8%
58
 
0.1%
62
 
< 0.1%
742
 
0.5%
853
 
0.6%
886742
80.2%
99699
 
8.3%
ValueCountFrequency (%)
99699
 
8.3%
886742
80.2%
853
 
0.6%
742
 
0.5%
62
 
< 0.1%
58
 
0.1%
464
 
0.8%
3249
 
3.0%
2174
 
2.1%
1139
 
1.7%

LQ_1EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
1.0
4701 
2.0
2629 
9.0
715 
3.0
 
127

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.04701
55.9%
2.02629
31.3%
9.0715
 
8.5%
3.0127
 
1.5%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:25.115642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:25.191407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.04701
57.5%
2.02629
32.2%
9.0715
 
8.7%
3.0127
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LQ_2EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
1.0
6677 
2.0
718 
9.0
715 
3.0
 
62

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06677
79.5%
2.0718
 
8.5%
9.0715
 
8.5%
3.062
 
0.7%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:25.281028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:25.342138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.06677
81.7%
2.0718
 
8.8%
9.0715
 
8.7%
3.062
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LQ_3EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
1.0
6027 
2.0
1322 
9.0
716 
3.0
 
107

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06027
71.7%
2.01322
 
15.7%
9.0716
 
8.5%
3.0107
 
1.3%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:25.429117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:25.492171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.06027
73.8%
2.01322
 
16.2%
9.0716
 
8.8%
3.0107
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LQ_4EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
1.0
4757 
2.0
2273 
9.0
721 
3.0
 
421

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.04757
56.6%
2.02273
27.0%
9.0721
 
8.6%
3.0421
 
5.0%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:25.566641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:25.642819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.04757
58.2%
2.02273
27.8%
9.0721
 
8.8%
3.0421
 
5.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LQ_5EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
1.0
6366 
2.0
980 
9.0
723 
3.0
 
103

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06366
75.8%
2.0980
 
11.7%
9.0723
 
8.6%
3.0103
 
1.2%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:25.708212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:25.792523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.06366
77.9%
2.0980
 
12.0%
9.0723
 
8.8%
3.0103
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BO1_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
1.0
5911 
2.0
1206 
3.0
880 
9.0
 
175

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row9.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.05911
70.3%
2.01206
 
14.4%
3.0880
 
10.5%
9.0175
 
2.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:26.009943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:26.177080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.05911
72.3%
2.01206
 
14.8%
3.0880
 
10.8%
9.0175
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BO2_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
4.0
4066 
1.0
2040 
2.0
1345 
3.0
568 
9.0
 
153

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row9.0
3rd row4.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
4.04066
48.4%
1.02040
24.3%
2.01345
 
16.0%
3.0568
 
6.8%
9.0153
 
1.8%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:26.356121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:26.486650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4.04066
49.8%
1.02040
25.0%
2.01345
 
16.5%
3.0568
 
7.0%
9.0153
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BD1_11
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean4.248286833
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:26.615510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.784189369
Coefficient of variation (CV)0.6553675583
Kurtosis-1.458207198
Mean4.248286833
Median Absolute Deviation (MAD)3
Skewness0.2669519743
Sum34717
Variance7.751710441
MonotonicityNot monotonic
2022-04-28T13:00:26.732426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
82084
24.8%
12017
24.0%
21191
14.2%
4929
11.1%
5672
 
8.0%
6589
 
7.0%
3529
 
6.3%
9161
 
1.9%
(Missing)231
 
2.7%
ValueCountFrequency (%)
12017
24.0%
21191
14.2%
3529
 
6.3%
4929
11.1%
5672
 
8.0%
6589
 
7.0%
82084
24.8%
9161
 
1.9%
ValueCountFrequency (%)
9161
 
1.9%
82084
24.8%
6589
 
7.0%
5672
 
8.0%
4929
11.1%
3529
 
6.3%
21191
14.2%
12017
24.0%

BD2_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean5.083700441
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:26.841900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.190687344
Coefficient of variation (CV)0.6276308728
Kurtosis-1.808885774
Mean5.083700441
Median Absolute Deviation (MAD)0
Skewness-0.233895706
Sum41544
Variance10.18048573
MonotonicityNot monotonic
2022-04-28T13:00:26.942991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
84101
48.8%
12093
24.9%
2931
 
11.1%
4378
 
4.5%
3366
 
4.4%
9164
 
2.0%
5139
 
1.7%
(Missing)231
 
2.7%
ValueCountFrequency (%)
12093
24.9%
2931
 
11.1%
3366
 
4.4%
4378
 
4.5%
5139
 
1.7%
84101
48.8%
9164
 
2.0%
ValueCountFrequency (%)
9164
 
2.0%
84101
48.8%
5139
 
1.7%
4378
 
4.5%
3366
 
4.4%
2931
 
11.1%
12093
24.9%

BD2_31
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean5.11123348
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:27.048105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.194657469
Coefficient of variation (CV)0.6250267145
Kurtosis-1.767996375
Mean5.11123348
Median Absolute Deviation (MAD)0
Skewness-0.2758776041
Sum41769
Variance10.20583634
MonotonicityNot monotonic
2022-04-28T13:00:27.147558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
84101
48.8%
12352
28.0%
2479
 
5.7%
4441
 
5.2%
3386
 
4.6%
5247
 
2.9%
9166
 
2.0%
(Missing)231
 
2.7%
ValueCountFrequency (%)
12352
28.0%
2479
 
5.7%
3386
 
4.6%
4441
 
5.2%
5247
 
2.9%
84101
48.8%
9166
 
2.0%
ValueCountFrequency (%)
9166
 
2.0%
84101
48.8%
5247
 
2.9%
4441
 
5.2%
3386
 
4.6%
2479
 
5.7%
12352
28.0%

dr_month
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing392
Missing (%)4.7%
Memory size65.8 KiB
0.0
5292 
1.0
2719 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05292
63.0%
1.02719
32.4%
(Missing)392
 
4.7%

Length

2022-04-28T13:00:27.260046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:27.341838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05292
66.1%
1.02719
33.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BP16_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct82
Distinct (%)1.0%
Missing325
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean514.7717257
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:27.596709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median360
Q3450
95-th percentile600
Maximum9999
Range9998
Interquartile range (IQR)443

Descriptive statistics

Standard deviation1598.472875
Coefficient of variation (CV)3.105207211
Kurtosis30.63139332
Mean514.7717257
Median Absolute Deviation (MAD)180
Skewness5.651130627
Sum4158326
Variance2555115.531
MonotonicityNot monotonic
2022-04-28T13:00:27.807148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
420816
 
9.7%
480766
 
9.1%
7718
 
8.5%
6626
 
7.4%
9596
 
7.1%
360538
 
6.4%
8521
 
6.2%
5494
 
5.9%
540391
 
4.7%
450369
 
4.4%
Other values (72)2243
26.7%
(Missing)325
 
3.9%
ValueCountFrequency (%)
13
 
< 0.1%
215
 
0.2%
379
 
0.9%
4214
 
2.5%
5494
5.9%
6626
7.4%
7718
8.5%
8521
6.2%
9596
7.1%
1081
 
1.0%
ValueCountFrequency (%)
9999219
2.6%
8401
 
< 0.1%
7802
 
< 0.1%
7209
 
0.1%
7051
 
< 0.1%
7001
 
< 0.1%
6905
 
0.1%
6701
 
< 0.1%
66051
 
0.6%
63029
 
0.3%

BP16_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct83
Distinct (%)1.0%
Missing324
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean524.283946
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:27.977718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median360
Q3480
95-th percentile600
Maximum9999
Range9998
Interquartile range (IQR)473

Descriptive statistics

Standard deviation1612.061172
Coefficient of variation (CV)3.074786447
Kurtosis29.96907585
Mean524.283946
Median Absolute Deviation (MAD)210
Skewness5.59196182
Sum4235690
Variance2598741.221
MonotonicityNot monotonic
2022-04-28T13:00:28.126326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480825
 
9.8%
420802
 
9.5%
7720
 
8.6%
9610
 
7.3%
6600
 
7.1%
8542
 
6.5%
360499
 
5.9%
5468
 
5.6%
540439
 
5.2%
450329
 
3.9%
Other values (73)2245
26.7%
(Missing)324
 
3.9%
ValueCountFrequency (%)
13
 
< 0.1%
215
 
0.2%
371
 
0.8%
4207
 
2.5%
5468
5.6%
6600
7.1%
7720
8.6%
8542
6.5%
9610
7.3%
10103
 
1.2%
ValueCountFrequency (%)
9999223
2.7%
11401
 
< 0.1%
8401
 
< 0.1%
7805
 
0.1%
7501
 
< 0.1%
72017
 
0.2%
7051
 
< 0.1%
7001
 
< 0.1%
6909
 
0.1%
6701
 
< 0.1%

BP1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
3.0
4073 
4.0
2419 
2.0
1182 
1.0
 
309
9.0
 
189

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row9.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.04073
48.5%
4.02419
28.8%
2.01182
 
14.1%
1.0309
 
3.7%
9.0189
 
2.2%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:28.248000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:28.315826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.04073
49.8%
4.02419
29.6%
2.01182
 
14.5%
1.0309
 
3.8%
9.0189
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

mh_stress
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing420
Missing (%)5.0%
Memory size65.8 KiB
0.0
6492 
1.0
1491 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06492
77.3%
1.01491
 
17.7%
(Missing)420
 
5.0%

Length

2022-04-28T13:00:28.398451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:28.462304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06492
81.3%
1.01491
 
18.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS1_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
3.0
4996 
2.0
2940 
9.0
 
175
1.0
 
61

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row9.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.04996
59.5%
2.02940
35.0%
9.0175
 
2.1%
1.061
 
0.7%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:28.528451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:28.618215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.04996
61.1%
2.02940
36.0%
9.0175
 
2.1%
1.061
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS3_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
4996 
3.0
2256 
1.0
671 
9.0
 
177
2.0
 
72

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row9.0
3rd row3.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.04996
59.5%
3.02256
26.8%
1.0671
 
8.0%
9.0177
 
2.1%
2.072
 
0.9%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:28.712960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:28.793745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.04996
61.1%
3.02256
27.6%
1.0671
 
8.2%
9.0177
 
2.2%
2.072
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS3_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)0.3%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean810.7442487
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:28.891483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q1888
median888
Q3888
95-th percentile888
Maximum999
Range998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation253.189052
Coefficient of variation (CV)0.3122921346
Kurtosis6.037310379
Mean810.7442487
Median Absolute Deviation (MAD)0
Skewness-2.823799207
Sum6625402
Variance64104.69606
MonotonicityNot monotonic
2022-04-28T13:00:29.059036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8887252
86.3%
10189
 
2.2%
999177
 
2.1%
20174
 
2.1%
561
 
0.7%
1561
 
0.7%
756
 
0.7%
340
 
0.5%
629
 
0.3%
222
 
0.3%
Other values (15)111
 
1.3%
(Missing)231
 
2.7%
ValueCountFrequency (%)
114
 
0.2%
222
 
0.3%
340
 
0.5%
420
 
0.2%
561
 
0.7%
629
 
0.3%
756
 
0.7%
85
 
0.1%
94
 
< 0.1%
10189
2.2%
ValueCountFrequency (%)
999177
 
2.1%
8887252
86.3%
402
 
< 0.1%
3016
 
0.2%
261
 
< 0.1%
255
 
0.1%
20174
 
2.1%
182
 
< 0.1%
176
 
0.1%
163
 
< 0.1%

BS3_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct19
Distinct (%)0.2%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean87.55910426
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:29.189686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile88
Q188
median88
Q388
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.434307845
Coefficient of variation (CV)0.08490616605
Kurtosis102.1420135
Mean87.55910426
Median Absolute Deviation (MAD)0
Skewness-9.844473898
Sum715533
Variance55.26893313
MonotonicityNot monotonic
2022-04-28T13:00:29.306838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
887923
94.3%
99177
 
2.1%
2017
 
0.2%
1011
 
0.1%
510
 
0.1%
77
 
0.1%
26
 
0.1%
43
 
< 0.1%
33
 
< 0.1%
63
 
< 0.1%
Other values (9)12
 
0.1%
(Missing)231
 
2.7%
ValueCountFrequency (%)
12
 
< 0.1%
26
0.1%
33
 
< 0.1%
43
 
< 0.1%
510
0.1%
63
 
< 0.1%
77
0.1%
91
 
< 0.1%
1011
0.1%
122
 
< 0.1%
ValueCountFrequency (%)
99177
 
2.1%
887923
94.3%
301
 
< 0.1%
252
 
< 0.1%
241
 
< 0.1%
2017
 
0.2%
181
 
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
122
 
< 0.1%

BS12_47
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7899 
9.0
 
176
2.0
 
55
3.0
 
26
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row9.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07899
94.0%
9.0176
 
2.1%
2.055
 
0.7%
3.026
 
0.3%
1.016
 
0.2%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:29.427065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:29.507297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07899
96.7%
9.0176
 
2.2%
2.055
 
0.7%
3.026
 
0.3%
1.016
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS8_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
5282 
2.0
2496 
1.0
 
216
9.0
 
178

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row9.0
3rd row2.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.05282
62.9%
2.02496
29.7%
1.0216
 
2.6%
9.0178
 
2.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:29.602412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:29.674593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.05282
64.6%
2.02496
30.5%
1.0216
 
2.6%
9.0178
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS9_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
3.0
6954 
2.0
823 
1.0
 
221
9.0
 
174

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row9.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.06954
82.8%
2.0823
 
9.8%
1.0221
 
2.6%
9.0174
 
2.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:29.757369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:29.824591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.06954
85.1%
2.0823
 
10.1%
1.0221
 
2.7%
9.0174
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS13
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
2.0
7347 
1.0
 
642
9.0
 
183

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row9.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.07347
87.4%
1.0642
 
7.6%
9.0183
 
2.2%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:29.903312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:29.972045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.07347
89.9%
1.0642
 
7.9%
9.0183
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sm_presnt
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing408
Missing (%)4.9%
Memory size65.8 KiB
0.0
7254 
1.0
741 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07254
86.3%
1.0741
 
8.8%
(Missing)408
 
4.9%

Length

2022-04-28T13:00:30.044997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:30.107589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07254
90.7%
1.0741
 
9.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_71
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
2.0
7401 
9.0
 
725
1.0
 
46

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.07401
88.1%
9.0725
 
8.6%
1.046
 
0.5%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:30.170079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:30.365054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.07401
90.6%
9.0725
 
8.9%
1.046
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_72
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean8.065834557
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:30.432331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4548627657
Coefficient of variation (CV)0.05639376341
Kurtosis107.212905
Mean8.065834557
Median Absolute Deviation (MAD)0
Skewness-7.1751687
Sum65914
Variance0.2069001356
MonotonicityNot monotonic
2022-04-28T13:00:30.521523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
87401
88.1%
9725
 
8.6%
713
 
0.2%
312
 
0.1%
19
 
0.1%
25
 
0.1%
53
 
< 0.1%
62
 
< 0.1%
42
 
< 0.1%
(Missing)231
 
2.7%
ValueCountFrequency (%)
19
 
0.1%
25
 
0.1%
312
 
0.1%
42
 
< 0.1%
53
 
< 0.1%
62
 
< 0.1%
713
 
0.2%
87401
88.1%
9725
 
8.6%
ValueCountFrequency (%)
9725
 
8.6%
87401
88.1%
713
 
0.2%
62
 
< 0.1%
53
 
< 0.1%
42
 
< 0.1%
312
 
0.1%
25
 
0.1%
19
 
0.1%

BE3_81
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
2.0
7206 
9.0
725 
1.0
 
241

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.07206
85.8%
9.0725
 
8.6%
1.0241
 
2.9%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:30.628234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:30.695308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.07206
88.2%
9.0725
 
8.9%
1.0241
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_82
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean7.952031326
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:30.758195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9202169321
Coefficient of variation (CV)0.1157209893
Kurtosis34.1322839
Mean7.952031326
Median Absolute Deviation (MAD)0
Skewness-5.478409892
Sum64984
Variance0.8467992021
MonotonicityNot monotonic
2022-04-28T13:00:30.848148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
87206
85.8%
9729
 
8.7%
363
 
0.7%
148
 
0.6%
247
 
0.6%
735
 
0.4%
427
 
0.3%
511
 
0.1%
66
 
0.1%
(Missing)231
 
2.7%
ValueCountFrequency (%)
148
 
0.6%
247
 
0.6%
363
 
0.7%
427
 
0.3%
511
 
0.1%
66
 
0.1%
735
 
0.4%
87206
85.8%
9729
 
8.7%
ValueCountFrequency (%)
9729
 
8.7%
87206
85.8%
735
 
0.4%
66
 
0.1%
511
 
0.1%
427
 
0.3%
363
 
0.7%
247
 
0.6%
148
 
0.6%

BE3_75
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
2.0
7303 
9.0
731 
1.0
 
138

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.07303
86.9%
9.0731
 
8.7%
1.0138
 
1.6%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:30.953033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:31.023599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.07303
89.4%
9.0731
 
8.9%
1.0138
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_76
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean8.024106706
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:31.088174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.632053974
Coefficient of variation (CV)0.07876938794
Kurtosis56.23157297
Mean8.024106706
Median Absolute Deviation (MAD)0
Skewness-6.252641638
Sum65573
Variance0.399492226
MonotonicityNot monotonic
2022-04-28T13:00:31.162810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
87303
86.9%
9731
 
8.7%
527
 
0.3%
326
 
0.3%
723
 
0.3%
421
 
0.2%
221
 
0.2%
610
 
0.1%
110
 
0.1%
(Missing)231
 
2.7%
ValueCountFrequency (%)
110
 
0.1%
221
 
0.2%
326
 
0.3%
421
 
0.2%
527
 
0.3%
610
 
0.1%
723
 
0.3%
87303
86.9%
9731
 
8.7%
ValueCountFrequency (%)
9731
 
8.7%
87303
86.9%
723
 
0.3%
610
 
0.1%
527
 
0.3%
421
 
0.2%
326
 
0.3%
221
 
0.2%
110
 
0.1%

BE3_85
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
2.0
6468 
1.0
974 
9.0
730 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.06468
77.0%
1.0974
 
11.6%
9.0730
 
8.7%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:31.294787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:31.358042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.06468
79.1%
1.0974
 
11.9%
9.0730
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_86
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean7.626896721
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:31.420354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median8
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.47185932
Coefficient of variation (CV)0.192982726
Kurtosis7.864781518
Mean7.626896721
Median Absolute Deviation (MAD)0
Skewness-2.888603285
Sum62327
Variance2.166369857
MonotonicityNot monotonic
2022-04-28T13:00:31.508944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
86468
77.0%
9731
 
8.7%
3203
 
2.4%
5198
 
2.4%
7168
 
2.0%
2154
 
1.8%
4112
 
1.3%
171
 
0.8%
667
 
0.8%
(Missing)231
 
2.7%
ValueCountFrequency (%)
171
 
0.8%
2154
 
1.8%
3203
 
2.4%
4112
 
1.3%
5198
 
2.4%
667
 
0.8%
7168
 
2.0%
86468
77.0%
9731
 
8.7%
ValueCountFrequency (%)
9731
 
8.7%
86468
77.0%
7168
 
2.0%
667
 
0.8%
5198
 
2.4%
4112
 
1.3%
3203
 
2.4%
2154
 
1.8%
171
 
0.8%

BE3_91
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
2.0
3766 
1.0
3678 
9.0
728 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.03766
44.8%
1.03678
43.8%
9.0728
 
8.7%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:31.618347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:31.679876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.03766
46.1%
1.03678
45.0%
9.0728
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE8_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)0.3%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean20.08272149
Minimum0
Maximum99
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:31.747714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median9
Q313
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)7

Descriptive statistics

Standard deviation30.4253614
Coefficient of variation (CV)1.51500191
Kurtosis2.821562725
Mean20.08272149
Median Absolute Deviation (MAD)3
Skewness2.16953489
Sum164116
Variance925.7026166
MonotonicityNot monotonic
2022-04-28T13:00:31.842038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
991045
12.4%
10971
11.6%
8769
9.2%
5716
8.5%
6666
7.9%
12566
 
6.7%
7520
 
6.2%
4497
 
5.9%
9430
 
5.1%
3346
 
4.1%
Other values (14)1646
19.6%
ValueCountFrequency (%)
01
 
< 0.1%
150
 
0.6%
2194
 
2.3%
3346
4.1%
4497
5.9%
5716
8.5%
6666
7.9%
7520
6.2%
8769
9.2%
9430
5.1%
ValueCountFrequency (%)
991045
12.4%
221
 
< 0.1%
212
 
< 0.1%
2011
 
0.1%
196
 
0.1%
1853
 
0.6%
1766
 
0.8%
16107
 
1.3%
15262
 
3.1%
14288
 
3.4%

BE3_31
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean13.58174254
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:31.932606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)6

Descriptive statistics

Standard deviation27.87662382
Coefficient of variation (CV)2.052507161
Kurtosis5.436017037
Mean13.58174254
Median Absolute Deviation (MAD)3
Skewness2.707508521
Sum110990
Variance777.1061554
MonotonicityNot monotonic
2022-04-28T13:00:32.021371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
82191
26.1%
11953
23.2%
4860
 
10.2%
99780
 
9.3%
6653
 
7.8%
3585
 
7.0%
5467
 
5.6%
2388
 
4.6%
7295
 
3.5%
(Missing)231
 
2.7%
ValueCountFrequency (%)
11953
23.2%
2388
 
4.6%
3585
 
7.0%
4860
 
10.2%
5467
 
5.6%
6653
 
7.8%
7295
 
3.5%
82191
26.1%
99780
 
9.3%
ValueCountFrequency (%)
99780
 
9.3%
82191
26.1%
7295
 
3.5%
6653
 
7.8%
5467
 
5.6%
4860
 
10.2%
3585
 
7.0%
2388
 
4.6%
11953
23.2%

BE3_32
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct12
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean31.04185022
Minimum0
Maximum99
Zeros2858
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:32.111551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q388
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)88

Descriptive statistics

Standard deviation42.78004939
Coefficient of variation (CV)1.378141093
Kurtosis-1.468617036
Mean31.04185022
Median Absolute Deviation (MAD)1
Skewness0.7123627002
Sum253674
Variance1830.132626
MonotonicityNot monotonic
2022-04-28T13:00:32.196966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
02858
34.0%
881953
23.2%
11721
20.5%
99785
 
9.3%
2577
 
6.9%
3163
 
1.9%
944
 
0.5%
438
 
0.5%
521
 
0.2%
68
 
0.1%
Other values (2)4
 
< 0.1%
(Missing)231
 
2.7%
ValueCountFrequency (%)
02858
34.0%
11721
20.5%
2577
 
6.9%
3163
 
1.9%
438
 
0.5%
521
 
0.2%
68
 
0.1%
72
 
< 0.1%
82
 
< 0.1%
944
 
0.5%
ValueCountFrequency (%)
99785
9.3%
881953
23.2%
944
 
0.5%
82
 
< 0.1%
72
 
< 0.1%
68
 
0.1%
521
 
0.2%
438
 
0.5%
3163
 
1.9%
2577
 
6.9%

BE5_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing231
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean2.404919236
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:32.318351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.615841946
Coefficient of variation (CV)1.087704695
Kurtosis1.123674669
Mean2.404919236
Median Absolute Deviation (MAD)0
Skewness1.62103564
Sum19653
Variance6.842629084
MonotonicityNot monotonic
2022-04-28T13:00:32.420563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
16030
71.8%
6762
 
9.1%
9750
 
8.9%
4247
 
2.9%
3175
 
2.1%
5124
 
1.5%
284
 
1.0%
(Missing)231
 
2.7%
ValueCountFrequency (%)
16030
71.8%
284
 
1.0%
3175
 
2.1%
4247
 
2.9%
5124
 
1.5%
6762
 
9.1%
9750
 
8.9%
ValueCountFrequency (%)
9750
 
8.9%
6762
 
9.1%
5124
 
1.5%
4247
 
2.9%
3175
 
2.1%
284
 
1.0%
16030
71.8%

pa_aerobic
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1012
Missing (%)12.0%
Memory size65.8 KiB
0.0
5072 
1.0
2319 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05072
60.4%
1.02319
27.6%
(Missing)1012
 
12.0%

Length

2022-04-28T13:00:32.526737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:32.575938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05072
68.6%
1.02319
31.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI1_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
1.0
4401 
8.0
3601 
0.0
 
166
9.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row8.0
4th row8.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.04401
52.4%
8.03601
42.9%
0.0166
 
2.0%
9.04
 
< 0.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:32.668241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:32.721410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.04401
53.9%
8.03601
44.1%
0.0166
 
2.0%
9.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
5328 
1.0
2446 
0.0
 
392
9.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row8.0
4th row1.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.05328
63.4%
1.02446
29.1%
0.0392
 
4.7%
9.06
 
0.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:32.812498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:32.880715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.05328
65.2%
1.02446
29.9%
0.0392
 
4.8%
9.06
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI3_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7034 
9.0
 
666
1.0
 
380
0.0
 
92

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07034
83.7%
9.0666
 
7.9%
1.0380
 
4.5%
0.092
 
1.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:32.982008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:33.198368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07034
86.1%
9.0666
 
8.1%
1.0380
 
4.7%
0.092
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing906
Missing (%)10.8%
Memory size65.8 KiB
8.0
6848 
1.0
 
579
0.0
 
70

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.06848
81.5%
1.0579
 
6.9%
0.070
 
0.8%
(Missing)906
 
10.8%

Length

2022-04-28T13:00:33.286712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:33.356564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.06848
91.3%
1.0579
 
7.7%
0.070
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI5_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7249 
9.0
 
673
1.0
 
231
0.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07249
86.3%
9.0673
 
8.0%
1.0231
 
2.7%
0.019
 
0.2%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:33.453743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:33.519812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07249
88.7%
9.0673
 
8.2%
1.0231
 
2.8%
0.019
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DM2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
5215 
1.0
2069 
9.0
677 
0.0
 
211

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.05215
62.1%
1.02069
 
24.6%
9.0677
 
8.1%
0.0211
 
2.5%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:33.647928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:33.723907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.05215
63.8%
1.02069
 
25.3%
9.0677
 
8.3%
0.0211
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DM3_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7186 
9.0
 
677
1.0
 
250
0.0
 
59

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07186
85.5%
9.0677
 
8.1%
1.0250
 
3.0%
0.059
 
0.7%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:33.827742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:33.902364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07186
87.9%
9.0677
 
8.3%
1.0250
 
3.1%
0.059
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DM4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
5701 
1.0
1489 
9.0
680 
0.0
 
302

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row1.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.05701
67.8%
1.01489
 
17.7%
9.0680
 
8.1%
0.0302
 
3.6%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:33.992755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:34.062786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.05701
69.8%
1.01489
 
18.2%
9.0680
 
8.3%
0.0302
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DJ2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7004 
9.0
 
680
0.0
 
473
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07004
83.4%
9.0680
 
8.1%
0.0473
 
5.6%
1.015
 
0.2%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:34.140404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:34.207989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07004
85.7%
9.0680
 
8.3%
0.0473
 
5.8%
1.015
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DJ4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7134 
9.0
 
680
1.0
 
277
0.0
 
81

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row1.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07134
84.9%
9.0680
 
8.1%
1.0277
 
3.3%
0.081
 
1.0%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:34.296764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:34.382243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07134
87.3%
9.0680
 
8.3%
1.0277
 
3.4%
0.081
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DJ6_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7118 
9.0
 
684
0.0
 
223
1.0
 
147

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07118
84.7%
9.0684
 
8.1%
0.0223
 
2.7%
1.0147
 
1.7%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:34.461869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:34.528585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07118
87.1%
9.0684
 
8.4%
0.0223
 
2.7%
1.0147
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DJ8_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7001 
9.0
 
684
1.0
 
402
0.0
 
85

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row1.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07001
83.3%
9.0684
 
8.1%
1.0402
 
4.8%
0.085
 
1.0%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:34.610192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:34.678050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07001
85.7%
9.0684
 
8.4%
1.0402
 
4.9%
0.085
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI6_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7044 
9.0
 
675
1.0
 
394
0.0
 
59

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07044
83.8%
9.0675
 
8.0%
1.0394
 
4.7%
0.059
 
0.7%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:34.762429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:34.833406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07044
86.2%
9.0675
 
8.3%
1.0394
 
4.8%
0.059
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DF2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
6983 
9.0
 
684
1.0
 
330
0.0
 
175

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row1.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.06983
83.1%
9.0684
 
8.1%
1.0330
 
3.9%
0.0175
 
2.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:34.918040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:34.986819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.06983
85.5%
9.0684
 
8.4%
1.0330
 
4.0%
0.0175
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DL1_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7393 
9.0
 
684
1.0
 
75
0.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07393
88.0%
9.0684
 
8.1%
1.075
 
0.9%
0.020
 
0.2%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:35.086290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:35.198987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07393
90.5%
9.0684
 
8.4%
1.075
 
0.9%
0.020
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DE1_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
6333 
1.0
1781 
0.0
 
50
9.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row1.0

Common Values

ValueCountFrequency (%)
8.06333
75.4%
1.01781
 
21.2%
0.050
 
0.6%
9.08
 
0.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:35.401446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:35.498221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.06333
77.5%
1.01781
 
21.8%
0.050
 
0.6%
9.08
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DE2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7170 
9.0
 
683
1.0
 
202
0.0
 
117

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07170
85.3%
9.0683
 
8.1%
1.0202
 
2.4%
0.0117
 
1.4%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:35.601943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:35.690973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07170
87.7%
9.0683
 
8.4%
1.0202
 
2.5%
0.0117
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DH4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7174 
9.0
 
685
0.0
 
223
1.0
 
90

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07174
85.4%
9.0685
 
8.2%
0.0223
 
2.7%
1.090
 
1.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:35.790413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:35.879757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07174
87.8%
9.0685
 
8.4%
0.0223
 
2.7%
1.090
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC1_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7341 
9.0
 
683
0.0
 
109
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07341
87.4%
9.0683
 
8.1%
0.0109
 
1.3%
1.039
 
0.5%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:36.041196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:36.144920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07341
89.8%
9.0683
 
8.4%
0.0109
 
1.3%
1.039
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7470 
9.0
 
683
1.0
 
13
0.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07470
88.9%
9.0683
 
8.1%
1.013
 
0.2%
0.06
 
0.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:36.252639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:36.477660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07470
91.4%
9.0683
 
8.4%
1.013
 
0.2%
0.06
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC3_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7367 
9.0
 
683
0.0
 
90
1.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07367
87.7%
9.0683
 
8.1%
0.090
 
1.1%
1.032
 
0.4%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:36.581620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:36.654016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07367
90.1%
9.0683
 
8.4%
0.090
 
1.1%
1.032
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7406 
9.0
 
683
0.0
 
49
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07406
88.1%
9.0683
 
8.1%
0.049
 
0.6%
1.034
 
0.4%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:36.755272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:36.832462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07406
90.6%
9.0683
 
8.4%
0.049
 
0.6%
1.034
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC5_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7425 
9.0
 
683
0.0
 
61
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07425
88.4%
9.0683
 
8.1%
0.061
 
0.7%
1.03
 
< 0.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:36.926073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:37.003346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07425
90.9%
9.0683
 
8.4%
0.061
 
0.7%
1.03
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC6_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7439 
9.0
 
683
1.0
 
30
0.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07439
88.5%
9.0683
 
8.1%
1.030
 
0.4%
0.020
 
0.2%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:37.089389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:37.163446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07439
91.0%
9.0683
 
8.4%
1.030
 
0.4%
0.020
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC7_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7396 
9.0
 
683
0.0
 
50
1.0
 
43

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07396
88.0%
9.0683
 
8.1%
0.050
 
0.6%
1.043
 
0.5%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:37.257257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:37.329794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07396
90.5%
9.0683
 
8.4%
0.050
 
0.6%
1.043
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DK8_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7373 
9.0
 
688
0.0
 
59
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07373
87.7%
9.0688
 
8.2%
0.059
 
0.7%
1.052
 
0.6%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:37.429384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:37.499303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07373
90.2%
9.0688
 
8.4%
0.059
 
0.7%
1.052
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DK9_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7456 
9.0
 
688
0.0
 
18
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07456
88.7%
9.0688
 
8.2%
0.018
 
0.2%
1.010
 
0.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:37.585264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:37.655077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07456
91.2%
9.0688
 
8.4%
0.018
 
0.2%
1.010
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DK4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing231
Missing (%)2.7%
Memory size65.8 KiB
8.0
7437 
9.0
 
688
1.0
 
36
0.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.07437
88.5%
9.0688
 
8.2%
1.036
 
0.4%
0.011
 
0.1%
(Missing)231
 
2.7%

Length

2022-04-28T13:00:37.734901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:37.800725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
8.07437
91.0%
9.0688
 
8.4%
1.036
 
0.4%
0.011
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_Upro
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)0.1%
Missing612
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean0.2255166217
Minimum0
Maximum5
Zeros6561
Zeros (%)78.1%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:37.864517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5944509441
Coefficient of variation (CV)2.635951796
Kurtosis11.5778042
Mean0.2255166217
Median Absolute Deviation (MAD)0
Skewness3.178098826
Sum1757
Variance0.353371925
MonotonicityNot monotonic
2022-04-28T13:00:37.968273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
06561
78.1%
1842
 
10.0%
2275
 
3.3%
391
 
1.1%
418
 
0.2%
54
 
< 0.1%
(Missing)612
 
7.3%
ValueCountFrequency (%)
06561
78.1%
1842
 
10.0%
2275
 
3.3%
391
 
1.1%
418
 
0.2%
54
 
< 0.1%
ValueCountFrequency (%)
54
 
< 0.1%
418
 
0.2%
391
 
1.1%
2275
 
3.3%
1842
 
10.0%
06561
78.1%

HE_THfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6490 
9.0
1650 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06490
77.2%
9.01650
 
19.6%
1.02
 
< 0.1%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:38.065064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:38.128920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06490
79.7%
9.01650
 
20.3%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_THfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6719 
9.0
1387 
1.0
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06719
80.0%
9.01387
 
16.5%
1.036
 
0.4%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:38.198769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:38.263605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06719
82.5%
9.01387
 
17.0%
1.036
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_THfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6848 
9.0
1031 
8.0
 
162
1.0
 
101

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06848
81.5%
9.01031
 
12.3%
8.0162
 
1.9%
1.0101
 
1.2%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:38.369311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:38.436150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06848
84.1%
9.01031
 
12.7%
8.0162
 
2.0%
1.0101
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HBfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6485 
9.0
1651 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06485
77.2%
9.01651
 
19.6%
1.06
 
0.1%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:38.523423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:38.588056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06485
79.6%
9.01651
 
20.3%
1.06
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HBfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6742 
9.0
1389 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06742
80.2%
9.01389
 
16.5%
1.011
 
0.1%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:38.662599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:38.726430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06742
82.8%
9.01389
 
17.1%
1.011
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HBfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6916 
9.0
1034 
8.0
 
162
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06916
82.3%
9.01034
 
12.3%
8.0162
 
1.9%
1.030
 
0.4%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:38.798775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:38.858706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06916
84.9%
9.01034
 
12.7%
8.0162
 
2.0%
1.030
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_fh
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
3752 
1.0
3718 
9.0
672 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03752
44.7%
1.03718
44.2%
9.0672
 
8.0%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:38.925501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:39.002630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.03752
46.1%
1.03718
45.7%
9.0672
 
8.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HPfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
5885 
9.0
1631 
1.0
626 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05885
70.0%
9.01631
 
19.4%
1.0626
 
7.4%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:39.074021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:39.262058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05885
72.3%
9.01631
 
20.0%
1.0626
 
7.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HPfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
5772 
9.0
1358 
1.0
1012 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05772
68.7%
9.01358
 
16.2%
1.01012
 
12.0%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:39.355404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:39.421665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05772
70.9%
9.01358
 
16.7%
1.01012
 
12.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HPfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
5523 
1.0
1480 
9.0
976 
8.0
 
163

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05523
65.7%
1.01480
 
17.6%
9.0976
 
11.6%
8.0163
 
1.9%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:39.486163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:39.552457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05523
67.8%
1.01480
 
18.2%
9.0976
 
12.0%
8.0163
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HLfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6455 
9.0
1666 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06455
76.8%
9.01666
 
19.8%
1.021
 
0.2%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:39.633222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:39.685884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06455
79.3%
9.01666
 
20.5%
1.021
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HLfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6700 
9.0
1401 
1.0
 
41

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06700
79.7%
9.01401
 
16.7%
1.041
 
0.5%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:39.767802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:39.832108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06700
82.3%
9.01401
 
17.2%
1.041
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HLfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6811 
9.0
1043 
8.0
 
163
1.0
 
125

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06811
81.1%
9.01043
 
12.4%
8.0163
 
1.9%
1.0125
 
1.5%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:39.906868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:39.991514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06811
83.7%
9.01043
 
12.8%
8.0163
 
2.0%
1.0125
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_IHDfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6416 
9.0
1654 
1.0
 
72

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06416
76.4%
9.01654
 
19.7%
1.072
 
0.9%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:40.074435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:40.141287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06416
78.8%
9.01654
 
20.3%
1.072
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_IHDfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6638 
9.0
1394 
1.0
 
110

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06638
79.0%
9.01394
 
16.6%
1.0110
 
1.3%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:40.206457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:40.274974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06638
81.5%
9.01394
 
17.1%
1.0110
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_IHDfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6798 
9.0
1031 
8.0
 
163
1.0
 
150

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06798
80.9%
9.01031
 
12.3%
8.0163
 
1.9%
1.0150
 
1.8%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:40.348933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:40.414756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06798
83.5%
9.01031
 
12.7%
8.0163
 
2.0%
1.0150
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_STRfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6015 
9.0
1592 
1.0
 
535

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06015
71.6%
9.01592
 
18.9%
1.0535
 
6.4%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:40.494542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:40.556379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06015
73.9%
9.01592
 
19.6%
1.0535
 
6.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_STRfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6265 
9.0
1345 
1.0
 
532

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06265
74.6%
9.01345
 
16.0%
1.0532
 
6.3%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:40.625200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:40.700974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06265
76.9%
9.01345
 
16.5%
1.0532
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_STRfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6664 
9.0
1012 
1.0
 
303
8.0
 
163

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06664
79.3%
9.01012
 
12.0%
1.0303
 
3.6%
8.0163
 
1.9%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:40.787216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:40.854965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06664
81.8%
9.01012
 
12.4%
1.0303
 
3.7%
8.0163
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_DMfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6330 
9.0
1646 
1.0
 
166

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06330
75.3%
9.01646
 
19.6%
1.0166
 
2.0%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:40.953406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:41.025215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06330
77.7%
9.01646
 
20.2%
1.0166
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_DMfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6382 
9.0
1370 
1.0
 
390

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06382
75.9%
9.01370
 
16.3%
1.0390
 
4.6%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:41.102137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:41.167577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06382
78.4%
9.01370
 
16.8%
1.0390
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_DMfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing261
Missing (%)3.1%
Memory size65.8 KiB
0.0
6044 
9.0
990 
1.0
945 
8.0
 
163

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06044
71.9%
9.0990
 
11.8%
1.0945
 
11.2%
8.0163
 
1.9%
(Missing)261
 
3.1%

Length

2022-04-28T13:00:41.237753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:41.305734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06044
74.2%
9.0990
 
12.2%
1.0945
 
11.6%
8.0163
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing280
Missing (%)3.3%
Memory size65.8 KiB
3.0
5136 
2.0
1679 
1.0
1308 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.05136
61.1%
2.01679
 
20.0%
1.01308
 
15.6%
(Missing)280
 
3.3%

Length

2022-04-28T13:00:41.404074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:41.468077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.05136
63.2%
2.01679
 
20.7%
1.01308
 
16.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_obe
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.1%
Missing355
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean2.878230616
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:41.515059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9487072695
Coefficient of variation (CV)0.3296147516
Kurtosis-0.703233572
Mean2.878230616
Median Absolute Deviation (MAD)1
Skewness0.3144840231
Sum23164
Variance0.9000454833
MonotonicityNot monotonic
2022-04-28T13:00:41.613524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
23156
37.6%
32276
27.1%
42121
25.2%
1239
 
2.8%
5235
 
2.8%
621
 
0.2%
(Missing)355
 
4.2%
ValueCountFrequency (%)
1239
 
2.8%
23156
37.6%
32276
27.1%
42121
25.2%
5235
 
2.8%
621
 
0.2%
ValueCountFrequency (%)
621
 
0.2%
5235
 
2.8%
42121
25.2%
32276
27.1%
23156
37.6%
1239
 
2.8%

HE_DM_HbA1c
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing1034
Missing (%)12.3%
Memory size65.8 KiB
2.0
2818 
1.0
2504 
3.0
2047 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.02818
33.5%
1.02504
29.8%
3.02047
24.4%
(Missing)1034
 
12.3%

Length

2022-04-28T13:00:41.701258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:41.765402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.02818
38.2%
1.02504
34.0%
3.02047
27.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HCHOL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1032
Missing (%)12.3%
Memory size65.8 KiB
0.0
4726 
1.0
2645 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04726
56.2%
1.02645
31.5%
(Missing)1032
 
12.3%

Length

2022-04-28T13:00:41.967667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:42.028509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04726
64.1%
1.02645
35.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HTG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing1765
Missing (%)21.0%
Memory size65.8 KiB
0.0
5822 
1.0
816 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05822
69.3%
1.0816
 
9.7%
(Missing)1765
 
21.0%

Length

2022-04-28T13:00:42.093590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:42.158005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05822
87.7%
1.0816
 
12.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_hepaB
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing698
Missing (%)8.3%
Memory size65.8 KiB
0.0
7511 
1.0
 
194

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07511
89.4%
1.0194
 
2.3%
(Missing)698
 
8.3%

Length

2022-04-28T13:00:42.234793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:42.300347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07511
97.5%
1.0194
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_hepaC
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing698
Missing (%)8.3%
Memory size65.8 KiB
0.0
7585 
1.0
 
120

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07585
90.3%
1.0120
 
1.4%
(Missing)698
 
8.3%

Length

2022-04-28T13:00:42.366961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:42.430824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07585
98.4%
1.0120
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_anem
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing719
Missing (%)8.6%
Memory size65.8 KiB
0.0
6434 
1.0
1250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.06434
76.6%
1.01250
 
14.9%
(Missing)719
 
8.6%

Length

2022-04-28T13:00:42.497912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:42.550581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06434
83.7%
1.01250
 
16.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

T_NQ_OCP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing1125
Missing (%)13.4%
Memory size65.8 KiB
2.0
6079 
1.0
1082 
9.0
 
117

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.06079
72.3%
1.01082
 
12.9%
9.0117
 
1.4%
(Missing)1125
 
13.4%

Length

2022-04-28T13:00:42.626796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:42.681474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.06079
83.5%
1.01082
 
14.9%
9.0117
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

T_Q_VN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing1125
Missing (%)13.4%
Memory size65.8 KiB
2.0
5290 
1.0
1843 
9.0
 
117
3.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.05290
63.0%
1.01843
 
21.9%
9.0117
 
1.4%
3.028
 
0.3%
(Missing)1125
 
13.4%

Length

2022-04-28T13:00:42.752389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:42.819425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.05290
72.7%
1.01843
 
25.3%
9.0117
 
1.6%
3.028
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_BR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing965
Missing (%)11.5%
Memory size65.8 KiB
0.0
7032 
1.0
 
406

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07032
83.7%
1.0406
 
4.8%
(Missing)965
 
11.5%

Length

2022-04-28T13:00:42.894387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:42.958294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07032
94.5%
1.0406
 
5.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_LN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing965
Missing (%)11.5%
Memory size65.8 KiB
0.0
6880 
1.0
 
558

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06880
81.9%
1.0558
 
6.6%
(Missing)965
 
11.5%

Length

2022-04-28T13:00:43.022597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:43.083642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06880
92.5%
1.0558
 
7.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_DN
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing965
Missing (%)11.5%
Memory size65.8 KiB
0.0
7069 
1.0
 
369

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07069
84.1%
1.0369
 
4.4%
(Missing)965
 
11.5%

Length

2022-04-28T13:00:43.148613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:43.210719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07069
95.0%
1.0369
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_BR_FQ
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing959
Missing (%)11.4%
Memory size65.8 KiB
1.0
6899 
4.0
 
226
2.0
 
211
3.0
 
108

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06899
82.1%
4.0226
 
2.7%
2.0211
 
2.5%
3.0108
 
1.3%
(Missing)959
 
11.4%

Length

2022-04-28T13:00:43.261423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:43.342188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.06899
92.7%
4.0226
 
3.0%
2.0211
 
2.8%
3.0108
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_LN_FQ
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing959
Missing (%)11.4%
Memory size65.8 KiB
1.0
6782 
2.0
 
316
4.0
 
213
3.0
 
133

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06782
80.7%
2.0316
 
3.8%
4.0213
 
2.5%
3.0133
 
1.6%
(Missing)959
 
11.4%

Length

2022-04-28T13:00:43.421611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:43.507416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.06782
91.1%
2.0316
 
4.2%
4.0213
 
2.9%
3.0133
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_DN_FQ
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing959
Missing (%)11.4%
Memory size65.8 KiB
1.0
7130 
2.0
 
211
3.0
 
61
4.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.07130
84.9%
2.0211
 
2.5%
3.061
 
0.7%
4.042
 
0.5%
(Missing)959
 
11.4%

Length

2022-04-28T13:00:43.582730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:43.639630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.07130
95.8%
2.0211
 
2.8%
3.061
 
0.8%
4.042
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_OUT_FQ
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)0.1%
Missing959
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean5.460102096
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T13:00:43.709315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.384990379
Coefficient of variation (CV)0.2536564985
Kurtosis0.7598935293
Mean5.460102096
Median Absolute Deviation (MAD)1
Skewness-1.041808582
Sum40645
Variance1.91819835
MonotonicityNot monotonic
2022-04-28T13:00:43.789618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
62428
28.9%
51849
22.0%
71808
21.5%
4534
 
6.4%
3482
 
5.7%
2251
 
3.0%
192
 
1.1%
(Missing)959
 
11.4%
ValueCountFrequency (%)
192
 
1.1%
2251
 
3.0%
3482
 
5.7%
4534
 
6.4%
51849
22.0%
62428
28.9%
71808
21.5%
ValueCountFrequency (%)
71808
21.5%
62428
28.9%
51849
22.0%
4534
 
6.4%
3482
 
5.7%
2251
 
3.0%
192
 
1.1%

LS_1YR
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing959
Missing (%)11.4%
Memory size65.8 KiB
1.0
3963 
2.0
3481 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.03963
47.2%
2.03481
41.4%
(Missing)959
 
11.4%

Length

2022-04-28T13:00:43.900840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T13:00:43.962720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.03963
53.2%
2.03481
46.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-28T13:00:05.638087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:38.641575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:42.391901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:45.993412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:49.888224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:54.094484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:57.554561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:02.156701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:06.229806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:10.123811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:13.756154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:18.306776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:22.497262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:26.572171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:30.610491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:35.606612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:39.496416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:42.821382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:47.295698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:51.128004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:54.389222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:58.638252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T13:00:02.600430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T13:00:05.832404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:38.801811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:42.554747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:46.163585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:50.055322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:54.241457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:58:57.705292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:02.323592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:06.393845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:10.338275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:13.908746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:18.491642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:22.670145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:26.720041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:30.855220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:35.757622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:39.652882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:42.959707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:47.448051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:51.270038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-28T12:59:30.119531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-28T12:59:39.013102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-28T12:58:42.086722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-28T12:59:01.838248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-28T12:59:09.553214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-28T12:59:18.136824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-28T12:59:26.433701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:30.433701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:35.464982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:39.361839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:42.688150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:47.137125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:50.981882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:54.216584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:59:58.486825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T13:00:02.446774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T13:00:05.501407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-04-28T13:00:44.217636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-28T13:00:46.070303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-28T13:00:47.841976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-28T13:00:49.445854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-28T13:00:50.829723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-28T13:00:09.653047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-28T13:00:13.413134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-28T13:00:21.635506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IDyearsexageHE_htHE_wtHE_BMIM_2_yrM_2_rsLQ_1EQLLQ_2EQLLQ_3EQLLQ_4EQLLQ_5EQLBO1_1BO2_1BD1_11BD2_1BD2_31dr_monthBP16_1BP16_2BP1mh_stressBS1_1BS3_1BS3_2BS3_3BS12_47BS8_2BS9_2BS13sm_presntBE3_71BE3_72BE3_81BE3_82BE3_75BE3_76BE3_85BE3_86BE3_91BE8_1BE3_31BE3_32BE5_1pa_aerobicDI1_prDI2_prDI3_prDI4_prDI5_prDM2_prDM3_prDM4_prDJ2_prDJ4_prDJ6_prDJ8_prDI6_prDF2_prDL1_prDE1_prDE2_prDH4_prDC1_prDC2_prDC3_prDC4_prDC5_prDC6_prDC7_prDK8_prDK9_prDK4_prHE_UproHE_THfh1HE_THfh2HE_THfh3HE_HBfh1HE_HBfh2HE_HBfh3HE_fhHE_HPfh1HE_HPfh2HE_HPfh3HE_HLfh1HE_HLfh2HE_HLfh3HE_IHDfh1HE_IHDfh2HE_IHDfh3HE_STRfh1HE_STRfh2HE_STRfh3HE_DMfh1HE_DMfh2HE_DMfh3HE_HPHE_obeHE_DM_HbA1cHE_HCHOLHE_HTGHE_hepaBHE_hepaCHE_anemT_NQ_OCPT_Q_VNL_BRL_LNL_DNL_BR_FQL_LN_FQL_DN_FQL_OUT_FQLS_1YR
0b'A651228902'2016.02.078.0150.152.723.3910242.088.02.01.01.02.01.03.01.08.08.08.00.0480.0480.03.00.03.08.0888.088.08.08.03.02.00.02.08.02.08.02.08.02.08.01.012.05.02.01.01.01.01.08.08.08.01.08.08.08.08.00.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.08.08.00.00.00.00.00.00.00.01.00.01.00.00.01.00.00.01.00.00.00.00.00.00.00.03.02.02.01.00.01.00.00.02.02.0NaNNaNNaNNaNNaNNaNNaNNaN
1b'A651417601'2016.01.080.0155.261.625.5739192.088.01.01.01.01.02.09.09.09.09.09.0NaN390.0420.09.0NaN9.09.0999.099.09.09.09.09.0NaN2.08.02.08.02.08.02.08.02.010.06.01.01.00.01.01.08.08.08.08.08.08.08.01.08.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03.03.02.01.00.00.00.00.02.01.00.00.00.01.01.01.05.01.0
2b'A652228901'2016.01.066.0170.866.922.9324462.088.01.01.01.01.01.01.04.05.04.04.01.0390.0390.03.00.02.03.0888.088.08.02.03.01.00.02.08.02.08.02.08.02.08.01.010.04.00.01.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02.02.03.00.0NaN0.00.00.02.02.00.00.00.01.01.01.02.01.0
3b'A653184701'2016.02.075.0144.649.923.8651391.03.02.01.02.03.03.03.04.08.08.08.00.0360.0360.01.01.03.08.0888.088.08.08.03.02.00.02.08.02.08.02.08.02.08.02.099.01.088.01.00.08.01.08.08.08.08.08.01.08.08.08.08.08.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.00.01.00.00.01.02.02.02.01.00.00.00.01.01.02.00.00.00.01.02.01.07.02.0
4b'A653235705'2016.02.079.0147.977.735.5209581.02.02.01.01.02.01.01.01.05.01.01.01.0240.0240.03.00.03.08.0888.088.08.08.03.02.00.02.08.02.08.02.08.02.08.02.099.01.088.01.00.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.01.08.08.08.08.08.08.08.08.08.08.08.08.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03.03.03.00.00.00.00.01.02.02.00.01.00.01.02.01.07.02.0
5b'A653252701'2016.02.078.0145.352.124.6778032.088.02.01.01.02.01.01.04.02.01.01.00.0420.0420.04.00.03.08.0888.088.08.02.02.02.00.02.08.02.08.02.08.02.08.01.08.06.00.01.01.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.03.02.01.00.00.00.00.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaN
6b'A653302001'2016.01.066.0168.081.628.9115652.088.01.01.01.01.01.01.04.05.04.05.01.0420.0420.03.00.02.03.0888.088.08.08.03.02.00.02.08.02.08.02.08.02.08.01.010.03.00.01.00.01.01.08.08.08.08.08.08.08.08.08.08.08.08.08.01.08.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03.03.03.01.01.00.00.00.02.02.00.00.00.01.01.01.04.02.0
7b'A653370001'2016.01.075.0177.463.120.0503589.099.09.09.09.09.09.01.04.01.08.08.00.09999.09999.02.01.02.01.03.088.08.08.02.02.01.09.09.09.09.09.09.09.09.09.099.099.099.09.0NaN8.08.09.0NaN9.09.09.09.09.09.09.09.09.09.09.08.09.09.09.09.09.09.09.09.09.09.09.09.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.01.02.0NaNNaNNaNNaNNaNNaN2.02.00.00.00.01.01.01.07.01.0
8b'A653370002'2016.02.071.0153.451.922.0554869.099.09.09.09.09.09.01.02.01.08.08.00.09999.09999.02.01.03.08.0888.088.08.08.02.02.00.09.09.09.09.09.09.09.09.09.099.099.099.09.0NaN1.08.09.0NaN9.09.09.09.09.09.09.09.09.09.09.08.09.09.09.09.09.09.09.09.09.09.09.09.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.03.02.01.00.00.00.00.01.02.02.00.00.00.01.01.01.05.01.0
9b'A653383601'2016.02.068.0152.570.130.1424352.088.01.01.01.01.01.03.01.08.08.08.00.0300.0240.04.00.01.03.0888.088.08.02.03.02.00.02.08.02.08.02.08.02.08.01.012.08.01.01.01.01.08.08.08.08.01.08.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.01.01.01.01.00.00.00.00.00.00.00.00.00.00.00.00.03.03.02.00.00.00.00.00.02.02.00.00.00.01.01.01.05.01.0

Last rows

IDyearsexageHE_htHE_wtHE_BMIM_2_yrM_2_rsLQ_1EQLLQ_2EQLLQ_3EQLLQ_4EQLLQ_5EQLBO1_1BO2_1BD1_11BD2_1BD2_31dr_monthBP16_1BP16_2BP1mh_stressBS1_1BS3_1BS3_2BS3_3BS12_47BS8_2BS9_2BS13sm_presntBE3_71BE3_72BE3_81BE3_82BE3_75BE3_76BE3_85BE3_86BE3_91BE8_1BE3_31BE3_32BE5_1pa_aerobicDI1_prDI2_prDI3_prDI4_prDI5_prDM2_prDM3_prDM4_prDJ2_prDJ4_prDJ6_prDJ8_prDI6_prDF2_prDL1_prDE1_prDE2_prDH4_prDC1_prDC2_prDC3_prDC4_prDC5_prDC6_prDC7_prDK8_prDK9_prDK4_prHE_UproHE_THfh1HE_THfh2HE_THfh3HE_HBfh1HE_HBfh2HE_HBfh3HE_fhHE_HPfh1HE_HPfh2HE_HPfh3HE_HLfh1HE_HLfh2HE_HLfh3HE_IHDfh1HE_IHDfh2HE_IHDfh3HE_STRfh1HE_STRfh2HE_STRfh3HE_DMfh1HE_DMfh2HE_DMfh3HE_HPHE_obeHE_DM_HbA1cHE_HCHOLHE_HTGHE_hepaBHE_hepaCHE_anemT_NQ_OCPT_Q_VNL_BRL_LNL_DNL_BR_FQL_LN_FQL_DN_FQL_OUT_FQLS_1YR
8393b'R802410802'2020.02.065.0157.054.121.9481521.05.02.01.02.03.02.02.02.01.08.08.00.06.06.01.01.03.08.0888.088.08.08.03.02.00.02.08.02.08.02.08.02.08.01.06.08.00.05.00.08.01.08.08.08.01.08.01.08.01.01.08.08.08.08.01.08.08.08.08.08.08.08.08.08.08.08.08.00.09.00.00.09.00.00.01.09.01.01.09.00.00.09.00.00.09.00.00.09.00.00.01.02.03.01.00.00.00.00.02.02.00.00.00.01.01.01.07.01.0
8394b'R802412501'2020.01.074.0163.461.022.8468182.088.01.01.01.01.01.01.02.06.03.01.01.06.06.03.00.02.03.0888.088.08.02.03.02.00.02.08.02.08.02.08.02.08.01.06.08.00.01.01.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.02.02.00.00.00.00.00.02.02.00.00.00.01.01.01.06.02.0
8395b'R802412502'2020.02.075.0149.359.426.6481362.088.02.01.02.02.02.01.01.08.08.08.00.06.06.03.00.03.08.0888.088.08.02.03.02.00.02.08.02.08.02.08.02.08.01.06.08.01.01.01.01.08.08.08.08.01.08.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03.04.02.00.00.00.00.00.02.02.00.00.00.01.04.01.06.02.0
8396b'R802415902'2020.02.074.0159.368.627.0328952.088.02.01.02.02.01.03.04.01.08.08.00.07.07.03.00.03.08.0888.088.08.02.03.02.00.02.08.02.08.02.08.02.08.01.06.08.00.01.00.01.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.03.04.01.01.00.00.00.00.01.02.00.00.00.01.01.01.07.02.0
8397b'R803409101'2020.01.066.0163.664.223.9865852.088.01.01.01.01.01.01.04.02.02.02.00.06.08.03.00.03.08.0888.088.08.02.03.02.00.02.08.02.08.02.08.02.08.02.05.01.088.01.00.08.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.02.03.02.01.00.00.00.00.01.02.00.00.00.01.01.01.01.01.0
8398b'R804176202'2020.01.066.0163.770.526.3082382.088.01.02.01.01.01.01.01.04.02.02.01.08.08.04.00.03.08.0888.088.08.08.03.02.00.02.08.02.08.02.08.02.08.02.07.08.02.06.00.01.01.08.08.08.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.09.00.00.09.00.00.00.09.00.00.09.00.00.09.00.00.09.00.00.09.00.00.03.04.02.01.00.00.00.00.02.02.00.00.00.01.01.01.06.01.0
8399b'R804205101'2020.01.075.0163.169.526.1262382.088.01.01.01.01.01.01.02.03.01.01.01.05.05.02.01.03.08.0888.088.08.02.03.02.00.02.08.02.08.02.08.02.08.01.010.04.00.01.01.00.08.08.08.08.08.08.08.08.08.08.08.08.08.08.01.08.00.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02.04.03.00.01.00.00.00.02.01.00.00.00.01.01.01.06.01.0
8400b'R804205102'2020.02.065.0163.061.823.2601902.088.01.01.01.03.01.01.01.08.08.08.00.07.010.04.00.03.08.0888.088.08.08.03.01.00.02.08.02.08.02.08.02.08.02.05.02.02.01.00.08.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.02.01.00.00.00.00.02.02.00.00.00.01.01.01.06.01.0
8401b'R804344501'2020.02.065.0155.659.524.5752412.088.01.01.01.01.01.01.01.02.01.01.00.08.08.02.01.03.08.0888.088.08.02.03.02.00.02.08.02.08.02.08.02.08.02.010.02.00.01.00.08.08.08.08.08.08.08.08.00.00.08.00.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.08.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.02.03.03.00.01.00.00.00.02.02.00.00.00.01.01.03.04.01.0
8402b'R804371701'2020.01.065.0171.576.325.9415722.088.01.01.01.01.01.01.01.04.03.03.01.07.07.03.00.02.03.0888.088.08.08.03.02.00.02.08.02.08.02.08.02.08.01.04.08.04.06.00.08.01.08.08.08.08.08.08.08.08.08.08.08.08.08.08.01.08.08.08.08.08.08.08.08.08.08.08.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.02.04.02.01.00.00.00.00.01.02.00.00.00.01.01.01.06.01.0